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Publications

2013

  • B. Bischl, J. Julia Schiffner and C. Weihs. Benchmarking Classification Algorithms on High-Performance Computing Clusters. In M. Spiliopoulou, L. Schmidt Thieme and R. Jannings, editors, Data Analysis, Machine Learning and Knowledge DiscoveryStudies in Classification, Data Analysis and Knowledge Organization. Springer, accepted, 2013.
  • Stefan Hess, Tobias Wagner and Bernd Bischl. PROGRESS: Progressive Reinforcement-Learning-Based Surrogate Selection. In Learning and Intelligent Optimization Conference (LION), page accepted, 2013.
  • O. Mersmann, B. Bischl, H. Trautmann, M. Wagner, J. Bossek and F. Neumann. A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem. Annals of Mathematics and Artificial Intelligence, vol. March, pages 1-32, 2013.
  • O. Meyer, B. Bischl and C. Weihs. Support Vector Machines on Large Data Sets: Simple Parallel Approaches. In M. Spiliopoulou, L. Schmidt Thieme and R. Jannings, editors, Data Analysis, Machine Learning and Knowledge DiscoveryStudies in Classification, Data Analysis and Knowledge Organization. Springer, accepted, 2013.
  • Samadhi Nallaperuma, Markus Wagner, Frank Neumann, Bernd Bischl, Olaf Mersmann and Heike Trautmann. A Feature-Based Comparison of Local Search and the Christofides Algorithm for the Travelling Salesperson Problem. In Foundations of Genetic Algorithms (FOGA), page accepted, 2013.
  • I. Vatolkin. Statistical Comparison of Classifiers for Multi-Objective Feature Selection in Instrument Recognition. In M. Spiliopoulou, L. Schmidt Thieme and R. Jannings, editors, Data Analysis, Machine Learning and Knowledge DiscoveryStudies in Classification, Data Analysis and Knowledge Organization. Springer, accepted, 2013.

2012

  • Nadja Bauer, Julia Schiffner and Claus Weihs. Comparison of classical and sequential design of experiments in note onset detection. In Studies in Classification, Data Analysis, and Knowledge Organization, Berlin Heidelberg, 2012. Springer, accepted.
  • Nadja Bauer, Julia Schiffner and Claus Weihs. Einfluss der Musikinstrumente auf die Güte der Einsatzzeiterkennung. Technical report, , 2012.
  • B. Bischl. Resampling Methods for Meta-Model Validation with Recommendations for Evolutionary Computation. Evolutionary Computation, vol. 20 no. 2, 2012.
  • B. Bischl, M. Lang, O. Mersmann, J. Rahnenfuehrer and C. Weihs. BatchJobs and BatchExperiments: Abstraction mechanisms for using R in batch environments. Journal of Statistical Software, vol. submitted, 2012.
  • B. Bischl, M. Lang, O. Mersmann, J. Rahnenfuehrer and C. Weihs. Computing on high performance clusters with R: Packages BatchJobs and BatchExperiments. Technical report, , 2012.
  • B. Bischl and J. Schiffner. mlr: Machine Learning in R. 2012.
  • Bernd Bischl, Olaf Mersmann, Heike Trautmann and Mike Preuss. Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning. In Genetic and Evolutionary Computation Conference (GECCO), 2012.
  • P. Koch, B. Bischl, O. Flasch, T. Bartz Beielstein, C. Weihs and W. Konen. Tuning and evolution of support vector kernels. Evolutionary Intelligence, vol. 5 no. 3, pages 153-170, 2012.
  • Olaf Mersmann, Bernd Bischl, Jakob Bossek, Heike Trautmann, Wagner Markus and Frank Neumann. Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness. In Learning and Intelligent Optimization Conference (LION), 2012.
  • Olaf Mersmann, Mike Preuss, Trautmann, Heike, Bernd Bischl and Claus Weihs. Analyzing the BBOB Results by Means of Benchmarking Concepts. Evolutionary Computation Journal, accepted, 2012.
  • Julia Schiffner, Bernd Bischl and Claus Weihs. Bias-variance analysis of local classification methods. In Wolfgang Gaul, Andreas Geyer Schulz, Lars Schmidt Thieme and J. Kunze, editors, Challenges at the Interface of Data Analysis, Computer Science, and OptimizationStudies in Classification, Data Analysis, and Knowledge Organization, pages 49-57, Berlin Heidelberg, 2012. Springer.
  • Julia Schiffner, Erhard Godehardt, Stefanie Hillebrand, Alxander Albert, Arthur Lichtenberg and Claus Weihs. Identification of risk factors in coronary bypass surgery. In Studies in Classification, Data Analysis, and Knowledge Organization, Berlin Heidelberg, 2012. Springer, accepted.
  • C. Weihs, O. Mersmann, B. Bischl, A. Fritsch, H. Trautmann, T.M. Karbach and B. Spaan. A Case Study on the Use of Statistical Classification Methods in Particle Physics. In W. Gaul, A. Geyer Schulz, L. Schmidt Thieme and J. Kunze, editors, Challenges at the Interface of Data Analysis, Computer Science, and OptimizationStudies in Classification, Data Analysis, and Knowledge Organization, pages 69-77, Berlin Heidelberg, 2012. Springer.

2011

  • Nadja Bauer, Julia Schiffner and Claus Weihs. Comparison of classical and sequential design of experiments in note onset detection. Technical report, , 2011.
  • P. Koch, B. Bischl, O. Flasch, T. Bartz Beielstein and W. Konen. On the Tuning and Evolution of Support Vector Kernels. Technical report, , Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, 2011.
  • Nils Raabe. Physikalisch-Statistische Modellierung von Biegeschwingungen. , 2011.
  • Heike Riedel. Uncertainty Sampling: Anwendung zur Auswahl optimaler Sampler aus der trunkierten Normalverteilung sowie zur Klassifikation von Zelltypen im Mäusegehirn. , 2011.
  • Laura Schlieker. Klassifikation von Blutgefäßen und Neuronen des Gehirns anhand von ultramikroskopierten 3D-Bilddaten. , 2011.
  • R Development Core Team. R: A Language and Environment for Statistical Computing. 2011.
  • C. Weihs. Statistics for hearing aids: Auralization. In Second Bilateral German-Polish Symposium on Data Analysis and its Applications (GPSDAA), 2011.

2010

  • Alfonso Cano Pedro A. Forero. Consensus-Based Distributed Support Vector Machines. The Journal of Machine Learning Research, vol. 11, 2010.
  • Nadja Bauer, Julia Schiffner and Claus Weihs. Einsatzzeiterkennung bei polyphonen Musikzeitreihen. Technical report, , 2010.
  • B. Bischl. Resampling methods in model validation. In T. Bartz Beielstein, M. Chiarandini, L. Paquete and M. Preuss, editors, WEMACS - Proceedings of the Workshop on Experimental Methods for the Assessment of Computational Systems, Technical Report TR 10-2-007. Department of Computer Science, TU Dortmund University, 2010.
  • B. Bischl, M. Eichhoff and C. Weihs. Selecting Groups of Audio Features by Statistical Tests and the Group Lasso. In 9. ITG Fachtagung Sprachkommunikation, Berlin, Offenbach, 2010. VDE Verlag.
  • Bernd Bischl, Igor Vatolkin and Mike Preuss. Selecting Small Audio Feature Sets in Music Classification by Means of Asymmetric Mutation. In PPSN XI: Proceedings of the 11th International Conference on Parallel Problem Solving from Nature, volume 6238 of Lecture Notes in Computer Science, pages 314-323. Springer, 2010.
  • Eric Boendeu. Welche Wirkung hat die Einwilligung zur Marketing-Ansprache auf Kundenwerttreiber? - Eine Analyse des Vodafone-lMobilfunkbestandes mittels Propensity Score Matching. Master's thesis, Faculty of Statistics, TU Dortmund, Germany, 2010.
  • Michael Bücker, Gero Szepannek and Claus Weihs. Local Classification of Discrete Variables by Latent Class Models. In Classification as a Tool for Research, volume 40 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 127-135, Berlin Heidelberg, 2010. Springer.
  • H. Cheng, P.-N. Tan and R. Jin. Efficient Algorithm for Localized Support Vector Machine. IEEE Transactions on Knowledge and Data Engineering, vol. 22 no. 4, pages 537-549, 2010.
  • E. Dimitriadou, K. Hornik, F. Leisch, D. Meyer and A. Weingessel. e1071: Misc Functions of the Department of Statistics (e1071), TU Wien. 2010.
  • J. Ding, S. Wessing, H. Trautmann, J. Mehnen and B. Naujoks. Sequential Parameter Optimisation for Multi-Objective Evolutionary Optimisation of Additive Layer Manufacturing. In R. Teti, editors, CIRP ICME - 7th CIRP International Conference on Intelligent Computation in Manufacturing Engineering, Innovative and Cognitive Production Technology and Systems. forthcoming, 2010.
  • Abdaleziz Elkokhe. Six Sigma - Prozessoptimierung mit Hilfe statistischer Methoden am Beispiel eines Schokoriegels. Master's thesis, Faculty of Statistics, TU Dortmund, Germany, 2010.
  • M.J.A. Eugster, F. Leisch and C. Strobl. (Psycho-)Analysis of Benchmark Experiments - A Formal Framework for Investigating the Relationship between Data Sets and Learning Algorithms. Technical report, , 2010.
  • A. Frank, A. Asuncion and University of California, Irvine, School of Information and Computer Sciences. UCI Machine Learning Repository. 2010.
  • D. Ginsbourger and O. Roustant. DiceOptim: Kriging-based optimization for computer experiments. 2010.
  • I. Guyon, A. Saffari, G. Dror and G. Cawley. Model Selection: Beyond the Bayesian/Frequentist Divide. Journal of Machine Learning Research, vol. 11, pages 61-87, 2010.
  • Isabell Hoffmann. Quantilsschätzung zur Berechnung altersabhängiger Referenzlimits. , 2010.
  • Hristo Hristov. Optimierung der Bewertung der Performance von Korrosionsschutzmaterialien für den Automobilbau mittels statistischer Methoden (am Beispiel von Korrosionsschutzprimern). Master's thesis, Faculty of Statistics, TU Dortmund, Germany, 2010.
  • A. Ben Hur and J. Weston. , volume 609 of Methods in Molecular Biology, chapter A UserGuide to Support Vector Machines, pages 223-239. , 2010.
  • H. Locarek Junge and C. Weihs, editors. Classification as a Tool for Research, volume 40 of Studies in Classification, Data Analysis, and Knowledge Organization, Berlin Heidelberg, 2010. Springer.
  • I. Ben Khediri, C. Weihs and M. Limam. Support Vector Regression control charts for multivariate nonlinear autocorrelated processes. Chemometrics and Intelligent Laboratory Systems, vol. accepted, 2010. [DOI]
  • D. Klein. Lagrange Multipliers without Permanent Scarring. , 2010.
  • W. Konen, P. Koch, O. Flasch and T. Bartz Beielstein. Parameter-Tuned Data Mining: A General Framework. In F. Hoffmann and E. Hüllermeier, editors, Proceedings 20. Workshop Computational Intelligence. Universitätsverlag Karlsruhe, 2010.
  • Gerd Kopp, Ingor Baumann, Evelina Vogli, Wolfgang Tillmann and Claus Weihs. Desirability-Based Multi-Criteria Optimisation of HVOF Spray Experiments. In Classification as a Tool for Research, volume 40 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 811-818, Berlin Heidelberg, 2010. Springer, accepted.
  • S. Krey and U. Ligges. SVM Based Instrument and Timbre Classi cation. In H. Locarek Junge and C. Weihs, editors, Classification as a Tool for Research, pages 759-766, Berlin-Heidelberg-New York, 2010. Springer.
  • Konstantin Lang. Vergleich von Clusterverfahren bei Aufteilung von Tönen in drei Phasen. , 2010.
  • F. Leisch and E. Dimitriadou. mlbench: Machine Learning Benchmark Problems. R package version 2.0-0. 2010.
  • D. Lim, Y. Jin, Y.-S. Ong and B. Sendhoff. Generalizing surrogate-assisted evolutionary computation. IEEE Transactions on Evolutionary Computation, vol. 14 no. 3, pages 329-355, 2010. [DOI]
  • C. McKay. Automatic music classification with jMIR. PhD thesis, , 2010.
  • Stefan Meinke. Over-/Undersampling für unbalancierte Klassifikationsprobleme im Zwei-Klassen-Fall. , 2010.
  • O. Mersmann, M. Preuss and H. Trautmann. Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis. In R. Schaefer and others, editors, PPSN XI: Proceedings of the 11th International Conference on Parallel Problem Solving from Nature, volume 6238 of Lecture Notes in Computer Science, pages 73-82, Berlin, 2010. Springer.
  • O. Mersmann, M. Preuss and H. Trautmann. Benchmarking evolutionary algorithms: Towards exploratory landscape analysis. Technical report, , 2010.
  • O. Mersmann, H. Trautmann, B. Naujoks and C. Weihs. Benchmarking evolutionary multiobjective optimization algorithms. In Proceedings of the IEEE Congress on Evolutionary Computation 2010, pages 1311-1318. IEEE Press, 2010.
  • O. Mersmann, H. Trautmann, B. Naujoks and C. Weihs. On the Distribution of EMOA Hypervolumes. In Learning and Intelligent Optimization, volume 6073 of Lecture Notes in Computer Science, pages 333-337. Springer, 2010.
  • S. Mostaghim, H. Trautmann and O. Mersmann. Preference-Based MOPSO using Desirabilities. In R. Schaefer and others, editors, PPSN XI: Proceedings of the 11th International Conference on Parallel Problem Solving from Nature, volume 6238 of Lecture Notes in Computer Science, pages 101-110. Springer, 2010.
  • Tina Müller, Julia Schiffner, Holger Schwender, Gero Szepannek, Claus Weihs and Katja Ickstadt. Local analysis of SNP data. In H. Locarek Junge and C. Weihs, editors, Classification as a Tool for Research, volume 40 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 473-480, Berlin Heidelberg, 2010. Springer.
  • N. Raabe, D. Enk, D. Biermann and C. Weihs. Dynamic disturbances of BTO deep-hole drilling: Modelling chatter and spiralling as regenerative effects. In A. Fink, B. Lausen, W. Seidel and A. Ultsch, editors, Advances in Data Analysis, Data Handling and Business Intelligence, volume 38 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 745-754, Berlin Heidelberg, 2010. Springer.
  • Matthias Redecker. Instrumentenklassifizierung durch Kombination von Audiomerkmalen. , 2010.
  • J. Schiffner, G. Szepannek, T. Monthé and C. Weihs. Localized logistic regression for categorical influential factors. In A. Fink, B. Lausen, W. Seidel and A. Ultsch, editors, Advances in Data Analysis, Data Handling and Business Intelligence, volume 38 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 185-195, Berlin Heidelberg, 2010. Springer.
  • K. Schliep and K. Hechenbichler. kknn: Weighted k-Nearest Neighbors. 2010.
  • N. Segata and E. Blanzieri. Fast and Scalable Local Kernel Machines. Journal of Machine Learning Research, vol. 11, pages 1883-1926, 2010.
  • N. Segata and E. Blanzieri. Operators for transforming kernels into quasi-local kernels that improve SVM accuracy. Journal of Intelligent Information Systems, pages 1-32, 2010. [DOI]
  • B. Settles. Active learning literature survey. Technical report, , 2010.
  • K. Sommer and C. Weihs. Analysis of polyphonic music time series. In A. Fink, B. Lausen, W. Seidel and A. Ultsch, editors, Advances in Data Analysis, Data Handling and Business Intelligence, volume 38 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 429-437, Berlin Heidelberg, 2010. Springer.
  • Gero Szepannek, Matthias Gruhne, Bernd Bischl, Sebastian Krey, Tamás Harczos, Frank Klefenz, Dittmar Christian and Claus Weihs. Perceptually Based Phoneme Recognition in Popular Music. In H. Locarek Junge and Claus Weihs, editors, Classification as a Tool for Research, volume 40 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 751-758, Berlin Heidelberg, 2010. Springer.
  • R Development Core Team. R: A Language and Environment for Statistical Computing. 2010.
  • T.M. Therneau, B. Atkinson and B. R port by Ripley. rpart: Recursive Partitioning. 2010.
  • W. Tillmann, E. Vogli, I. Baumann, G. Kopp and C. Weihs. Desirability-Based Multi-Criteria Optimization of HVOF Spray Experiments to Manufacture Fine Structured Wear-Resistant 75Cr3C2-25(NiCr20) Coatings. Journal of Thermal Spray Technology, vol. 19, pages 392-408, 2010.
  • I. Vatolkin, W. Theimer and M. Botteck. AMUSE (Advanced Music Explorer) - A Multitool Framework for Music Data Analysis. In Proceedings of the 11th International Society for Music Information Retrieval Conference (ISMIR 2010), pages 33-38, 2010.
  • T. Voss, H. Trautmann and C. Igel. New Uncertainty Handling Strategies in Multi-Objective Evolutionary Optimization. In R. Schaefer and others, editors, PPSN XI: Proceedings of the 11th International Conference on Parallel Problem Solving from Nature, volume 6238 of Lecture Notes in Computer Science, pages 259-268. Springer, 2010.
  • T. Wagner, M. Emmerich, A. Deutz and W. Ponweiser. On Expected-Improvement Criteria for Model-Based Multi-Objective Optimization. In R. Schaefer, editors, Parallel Problem Solving from Nature (PPSN), pages 718-727. Springer, Berlin, 2010.
  • T. Wagner and H. Trautmann. Online Convergence Detection for Multiobjective Evolutionary Algorithms Revisited. In Proceedings of the IEEE Congress on Evolutionary Computation 2010, pages 3554-3561. IEEE Press, 2010.
  • T. Wagner and H. Trautmann. Integration of Preferences in Hypervolume-Based Multi-Objective Evolutionary Algorithms by Means of Desirability Functions. IEEE Transactions on Evolutionary Computation, Special Issue: Preference-based Multiobjective Evolutionary Algorithms, vol. accepted, 2010.
  • C. Weihs, A. Messaoud and N. Raabe. Control Charts Based on Models Derived from Differential Equations. Quality and Reliability Engineering International, vol. 26, pages 807-816, 2010. [DOI]
  • C. Weihs, Mersmann O., B. Bischl, A. Fritsch, H. Trautmann, T.-M. Karbach and B. Spaan. A Case Study on the Use of Statistical Classification Methods in Particle Physics. In MSDM2010, Tunis, 2010.
  • A. Younis and Z. Dong. Metamodelling using search space exploration and unimodal region elimination for design optimization. Engineering Optimization, vol. 6 no. 42, pages 517-533, 2010.

2009

  • A. Alexa and J. Rahnenführer. topGO: Enrichment analysis for Gene Ontology. 2009.
  • Y.T. Azene, R. Roy, D. Farrugia, C. Onisa, J. Mehnen and H. Trautmann. Work Roll Cooling System Design Optimisation in Presence of Uncertainty. In R. Roy and E. Shebab, editors, CIRP Design 2009 Conference, pages 57-64. Cranfield University Press, 2009.
  • Nadja Bauer. Automatische Variablenselektion für die Daten von Fitch Peer Analysis Report 2007. Master's thesis, Faculty of Statistics, TU Dortmund, Germany, 2009.
  • B. Bischl, U. Ligges and C. Weihs. Frequency estimation by DFT interpolation: A comparison of methods. Technical report, , 2009.
  • Bernd Bischl. Feature-Auswahl durch das Lernen multipler Kerne und eine Anwendung auf GO-Gruppen. Master's thesis, Faculty of Statistics, TU Dortmund, Germany, 2009.
  • B. Clarke, E. Fokoué and H.H. Zhang. Principles and Theory for Data Mining and Machine LearningSpringer Series in Statistics. Springer, New York, NY, USA, 2009.
  • H.J. Escalante, M. Montes and L.E. Sucar. Particle Swarm Model Selection. Journal of Machine Learning Research, vol. 10, pages 405-440, 2009.
  • M.J.A. Eugster and F. Leisch. Exploratory Analysis of Benchmark Experiments - An Interactive Approach. Technical report, , dfsdf, 2009.
  • V. Franc and S. Sonnenburg. Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization. J. Mach. Learn. Res., vol. 10, pages 2157-2192, 2009.
  • M. Gebel. Multivariate calibration of classifier scores into the probability space. , 2009.
  • T. Harczos, S. Werner, G. Szepannek and K. Brandenburg. Evaluation of Cues for Horizontal-Plane Localization with Bilateral Cochlear Implants. In Proc. Int. Symposium on Auditory and Audiological Research ISAARHelsingør, Denmark, 2009. accepted, 2009.
  • T. Hastie, R. Tibshirani and J. Friedman. Support Vector Machines and Flexible Discriminants. The Elements of Statistical Learning, pages 417-458, 2009. [DOI]
  • T. S original by Hastie, R. Tibshirani, F. Original R port by Leisch, K. Hornik and B.D. Ripley. mda: Mixture and flexible discriminant analysis. 2009.
  • K. Hornik and D. Meyer. relations: Data Structures and Algorithms for Relations. 2009.
  • J.-H. Kim. Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap. Computational Statistics and Data Analysis, vol. 53 no. 11, pages 3735-3745, 2009.
  • O. Mersmann. emoa: Evolutionary Multiobjective Optimization Algorithms. 2009.
  • Olaf Mersmann. Benchmarking evolutionary multiobjective optimization algorithms using R. , 2009.
  • A. Messaoud, W. Theis, F. Hering and C. Weihs. Monitoring a Drilling Process Using Residual Control Charts. Quality Engineering, vol. 21, pages 1-9, 2009.
  • A. Messaoud and C. Weihs. Monitoring a deep hole drilling process by nonlinear time series modeling. Journal of Sound and Vibration, vol. 321 no. 3-5, pages 620-630, 2009.
  • N. Mukhopadhyay and B. M. de Silva. Sequential methods and their applications. Chapman & Hall/CRC, 2009.
  • H. Nakayama, Y. Yun and M. Yoon. Sequential Approximate Multiobjective Optimization Using Computational Intelligence. Springer, Berlin, 2009.
  • B. Naujoks and H. Trautmann. Online Convergence Detection for Multiobjective Aerodynamic Applications. In IEEE Computational Intelligence Society and A. Tyrrell, editors, 2009 IEEE Congress on Evolutionary Computation, pages 332-339, Trondheim, Norway, 2009. IEEE Press.
  • Andreas Priefer. Text Mining am Beispiel von Produktdaten aus dem Telekommunikationssektor. , 2009.
  • A. Saffari, C. Leistner, J. Santner, M. Godec and H. Bischof. On-line Random Forests. 2009.
  • J. Schiffner and C. Weihs. D-optimal plans for variable selection in data bases. Technical report, , 2009.
  • R.I. Schmitt. Learning diagnostic rules with multivariate classification algorithms. , 2009.
  • B. Settles. Active Learning Literature Survey. Technical report, , 2009.
  • Shogun. A Large Scale Machine Learning Toolbox. 2009.
  • S.K. Smit and A.E. Eiben. Comparing Parameter Tuning Methods for Evolutionary Algorithms. In Andy Tyrrell, editors, IEEE Congress on Evolutionary Computation (CEC), pages 399-406. IEEE Press, Piscataway, NJ, 2009. [DOI]
  • G. Szepannek, T. Harczos, F. Klefenz and C. Weihs. Combining Different Auditory Model Based Feature Extraction Principles for Feature Enrichment in Automatic Speech Recognition. In A. Karpov, editors, Specom 2009 Proceedings, pages 205-210, 2009.
  • G. Szepannek, T. Harczos, F. Klefenz and C. Weihs. Extending Features for Automatic Speech Recognition by Means of Auditory Modelling. In Proceedings of the 17th European Signal Processing Conference, pages 1235-1239, 2009.
  • M.A. Taddy, H.K.H. Lee, G.A. Gray and J.D. Griffin. Bayesian Guided Pattern Search for Robust Local Optimization. In , pages 389-401, 2009.
  • Y. Tang, Y.-Q. Zhang, N.V. Chawla and S. Krasser. SVMs modeling for highly imbalanced classification. Trans. Sys. Man Cyber. Part B, vol. 39 no. 1, pages 281-288, 2009.
  • R Development Core Team. R: A Language and Environment for Statistical Computing. 2009.
  • W. Tillmann, E. Vogli, I. Baumann, G. Kopp and C. Weihs. Statistical Design of HVOF Spray Experiments to Manufacture Superfine Structured Wear Resistant Cr3C2 - 25(Ni 20Cr) Coatings. In B. R. Marple, M. M. Hyland, Y.-C. Lau, C.-J. Li, R. S. Lima and G. Montavon, editors, Thermal Spray 2009: Proceedings of the International Thermal Spray Conference (ITSC 09), pages 700-708, 2009.
  • H. Trautmann and J. Mehnen. Statistical Methods for Improving Multi-objective Evolutionary Optimisation. International Journal of Computational Intelligence Research, vol. 5 no. 2, pages 72-78, 2009.
  • H. Trautmann and J. Mehnen. Preference-Based Pareto-Optimization in Certain and Noisy Environments. Engineering Optimization, vol. 41, pages 23-38, 2009.
  • H. Trautmann, J. Mehnen and B. Naujoks. Pareto-Dominance in Noisy Environments. In IEEE Computational Intelligence Society and A. Tyrrell, editors, 2009 IEEE Congress on Evolutionary Computation, pages 3119-3126, Trondheim, Norway, 2009. IEEE Press.
  • H. Trautmann, T. Wagner, B. Naujoks, M. Preuss and J. Mehnen. Statistical Methods for Convergence Detection of Multiobjective Evolutionary Algorithms. Evolutionary Computation Journal, Special Issue: Twelve Years of EC Research in Dortmund, vol. 17 no. 4, pages 493-509, 2009.
  • T. Wagner, H. Trautmann and B. Naujoks. OCD: Online Convergence Detection for Evolutionary Multi-Objective Algorithms Based on Statistical Testing. In C. Fonseca and X. Gandibleux, editors, Evolutionary Multi-Criterion Optimization (EMO 2009), volume 5467 of Lecture Notes in Computer Science, pages 198-215, Berlin Heidelberg, 2009. Springer.
  • C. Weihs. Deriving a statistical model for the prediction of spiralling in BTA-deep-hole drilling from a physical model. In A. Okada, T. Imaizumi, H.-H. Bock and W. Gaul, editors, Cooperation in Classification and Data Analysis, volume 37 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 107-114. Springer, 2009.
  • C. Weihs and G. Szepannek. Distances in Classification. Transactions on Case-based Reasoning, vol. 2, pages 3-14, 2009.
  • H. Wickham. ggplot2: An implementation of the Grammar of Graphics. 2009.
  • Sebastian Wietzke. Verdichtung von Versicherungsdaten mit Hilfe der Methodik der Regressionsbäume. , 2009.
  • Y. Yun, M. Yoon and H. Nakayama. Multi-objective optimisation based on meta-modelling using support vector regression. Optimization and Engineering, vol. 10 no. 2, pages 167-181, 2009.
  • A. Zien, N. Kr䭥r, S. Sonnenburg and G. R䴳ch. The Feature Importance Ranking Measure. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2009.

2008

  • E. Alpaydin. Maschinelles Lernen. Oldenbourg, 2008.
  • G. Bansal, A.P. Sinha and H. Zhao. Tuning Data Mining Methods for Cost-Sensitive Regression: A Study in Loan Charge-Off Forecasting. JOURNAL OF MANAGEMENT INFORMATION SYSTEMS, vol. 25 no. 3, pages 315-336, 2008.
  • M. Bhattacharya. Meta Model Based \EAfor Complex Optimization. International Journal of Computational Intelligence, vol. 1 no. 4, pages 36-45, 2008.
  • H. Binder and M. Schumacher. Adapting Prediction Error Estimates for Biased Complexity Selection in High-Dimensional Bootstrap Samples. Statistical Applications in Genetics and Molecular Biology, vol. 7 no. 1, page 12, 2008.
  • Michael Bücker. Lokale Diskrimination diskreter Daten. , 2008.
  • S. Carbon, A. Ireland, C.J. Mungall, S. Shu, B. Marshall, S. Lewis, the A.G.O. Hub and the W.P.W. Group. AmiGO: online access to ontology and annotation data. Bioinformatics, 2008.
  • J. Chen and J. Ye. Training SVM with Indefinite Kernels. ICML, vol. 307, pages 136-143, 2008.
  • M.J.A. Eugster, T. Hothorn and F. Leisch. Exploratory and Inferential Analysis of Benchmark Experiments. Technical report, , 2008.
  • M.J.A. Eugster and F. Leisch. Bench Plot and Mixed Effects Models: First steps toward a comprehensive benchmark analysis toolbox. In P. Brito, editors, Compstat 2008--Proceedings in Computational Statistics, pages 299-306. Physica Verlag, Heidelberg, Germany, 2008.
  • M. Gebel and C. Weihs. Calibrating margin-based classifier scores into polychotomous assessment probabilities. In C. Preisach, H. Burkhardt, L. Schmidt Thieme and R. Decker, editors, Data Analysis, Machine Learning and Applications, volume 36 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 29-36, Berlin Heidelberg, 2008. Springer.
  • T. Glasmachers and C. Igel. Uncertainty Handling in Model Selection for Support Vector Machines. In PPSN, pages 185-194, 2008.
  • T. Glasmachers and C. Igel. Second-order smo improves svm online and active learning. Neural Comput., vol. 20 no. 2, pages 374-382, 2008.
  • M. Gönen and E. Alpaydin. Localized multiple kernel learning. In ICML Proceedings of the 25th international conference on Machine learning, pages 352-359, New York, NY, USA, 2008. ACM. [DOI]
  • M. Gönen and E. Alpaydin. Localized multiple kernel learning. In Proceedings of the 25th international conference on Machine learning (ICML, pages 352-359, 2008.
  • T. Harczos, S. Werner and G. Szepannek. Formant Map Counterpart in Auditory Processing Based on Cochlear Pressure Wave Trajectories. In Proceedings of IEEE Biomedical Cicuits and Systems Conference (BioCAS), pages 45-48, 2008.
  • Bernhard Schölkopf Thomas Hofmann and Alexander J. Smola. Kernel Methods in Machine Learning. The Annals of Statistics, vol. 36, pages 1171-1220, 2008.
  • D.J. Hunter. Essentials of Discrete MathematicsJones and Bartlett Publishers Series in Mathematics. Jones and Bartlett Publishers, 2008.
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  • J. Knowles and H. Nakayama. Multiobjective Optimization: Interactive and Evolutionary Approaches, chapter Meta-Modeling in Multiobjective Optimization, pages 245-284. Springer-Verlag, Berlin, Heidelberg, 2008.
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2007

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  • G. Szepannek, T. Harczos, F. Klefenz, A. Katai, P. Schikowski and C. Weihs. Vowel Classification by a Neurophysiologically Parametrized Auditory Model. In R. Decker and H.J. Lenz, editors, Advances in Data Analysis, volume 34 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 653-660, Berlin Heidelberg, 2007. Springer.
  • L.-Y. Tseng and C. Chen. Multiple Trajectory Search for Multiobjective Optimization. In IEEE Congress on Evolutionary Computation, pages 3609-3616, 2007.
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2006

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  • S. Sonnenburg, G. Rätsch, C. Schäfer and B. Schölkopf. Large scale multiple kernel learning. Journal of Machine Learning Research, vol. 7, pages 1531-1565, 2006.
  • J. Strackeljan, R. Jonscher, S. Prieur, D. Vogel, T. Deselaers, D. Keysers, A. Mauser, I. Bezrukov and A. Hegerath. GfKl Data Mining Competition 2005: Predicting Liquidity Crises of Companies. From Data and Information Analysis to Knowledge Engineering, pages 748-758, 2006. [DOI]
  • G. Szepannek, N. Raabe, O. Webber and C. Weihs. Prediction of Spiralling in BTA Deep-Hole Drilling - Estimating the SystemEigenfrequencies. Technical report, , 2006.
  • G. Szepannek and C. Weihs. Local Modelling in Classification on Different Feature Subspaces. Technical report, , 2006.
  • G. Szepannek and C. Weihs. Local Modelling in Classification on Different Feature Subspaces. In P. Perner, editors, Advances in Data Mining, volume 4065 of Lecture Notes in Artificial Intelligence, pages 226-238, Berlin Heidelberg, 2006. Springer.
  • G. Szepannek and C. Weihs. Variable Selection for Discrimination of More Than Two Classes Where Data are Sparse. In M. Spiliopoulou, R. Kruse, A. Nürnberger, C. Borgelt and W. Gaul, editors, From Data and Information Analysis to Knowledge Engineering, volume 31 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 700-707, Berlin Heidelberg, 2006. Springer.
  • G. Szepannek and C. Weihs. Explorative Development of Information Extraction Schemes for Speech Recognition from Simulated Auditory Neural Response Data via Parallel Local Hubel-Wiesel Networks. Technical report, , 2006.
  • A. Thomas, B. O, U. Ligges and S. Sturtz. Making BUGS Open. R News, vol. 6 no. 1, pages 12-17, 2006.
  • H. Trautmann and C. Weihs. On the Distribution of the Desirability Index using HarringtonDesirability Function. Metrika, vol. 63 no. 2, pages 207-213, 2006.
  • C. Weihs and U. Ligges. Parameter Optimization in Automatic Transcription of Music. In M. Spiliopoulou, R. Kruse, A. Nürnberger, C. Borgelt and W. Gaul, editors, From Data and Information Analysis to Knowledge Engineering, volume 31 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 740-747, Berlin Heidelberg, 2006. Springer.
  • C. Weihs, U. Ligges and K. Sommer. Analysis of Music Time Series. In A. Rizzi and M. Vichi, editors, COMPSTAT 2006 - Proceedings in Computational Statistics, pages 147-159, Heidelberg, 2006. Physica-Verlag.
  • C. Weihs, G. Szepannek, U. Ligges, K. Luebke and N. Raabe. Local Models in Register Classification by Timbre. In V. Batagelj, H.H. Bock, A. Ferligoj and A. , editors, Data Science and Classification, volume 32 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 316-322, Berlin Heidelberg, 2006. Springer.
  • P. Wolfrum, A. Gepperth, A. Sandamirskaya, O. Webber, N. Raabe, G. Szepannek and G. Schoener. Modelling and Understanding of Chatter. Technical report, , 2006.
  • L. Xu, K. Crammer and D. Schuurmans. Robust Support Vector Machine Training via Convex Outlier Ablation. , pages 536-542, 2006.
  • J. Yang. An Improved Cascade SVM Training Algorithm with Crossed Feedbacks. In IMSCCS Proceedings of the First International Multi-Symposiums on Computer and Computational Sciences - Volume 2 (IMSCCS, pages 735-738, Washington, DC, USA, 2006. IEEE Computer Society.
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2005

  • S. Abe. Support vector machines for pattern classification. Springer, London, 2005.
  • Susanne Balzer. Optimierte Bewertung von Service Monitoring Stichproben mit Hilfe typischer Fehlerbilder. Master's thesis, Faculty of Statistics, TU Dortmund, Germany, 2005.
  • A. Bordes, S. Ertekin, J. Weston and L. Bottou. Journal of Machine Learning Research, volume 6, chapter Fast Kernel Classifiers with Online and Active Learning, pages 1579-1619. , 2005.
  • A. Christmann, K. Luebke, M. Marin Galiano and S. Rüping. Determination of Hyper-parameters for Kernel Based Classification and Regression. Technical report, , 2005.
  • Irina Czogiel. Optimierung der Vorverarbeitung von LC/MS-Daten. , 2005.
  • Kerstin Dunker. Zeitreihenanalyse und Klassifikation digitaler Datenströme. , 2005.
  • D. Enache and C. Weihs. Importance Assessment of Correlated Predictors in Business Cycles Classification. In C. Weihs and W. Gaul, editors, Classification - the Ubiquitous Challenge, volume 29 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 545-552, Berlin Heidelberg, 2005. Springer.
  • H. Frohlich and A. Zell. Efficient parameter selection for support vector machines in classification and regression via model-based global optimization. In Neural Networks, 2005. IJCNN Proceedings. 2005 IEEE International Joint Conference on, volume 3, pages 1431-1436, 2005.
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  • P.I. Good. Resampling Methods: A Practical Guide to Data Analysis. Birkhauser, 2005.
  • H.P. Graf, E. Cosatto, L. Bottou, I. Durdanovic and V. Vapnik. Parallel support vector machines: The cascade svm. In In Advances in Neural Information Processing Systems, pages 521-528. MIT Press, 2005.
  • T. Hothorn, F. Leisch, A. Zeileis and K. Hornik. The Design and Analysis of Benchmark Experiments. Journal of Computational and Graphical Statistics, vol. 14, pages 675-699, 2005.
  • J. Jessenberger and C. Weihs. Desirability to characterize process capability. In C. Weihs and W. Gaul, editors, Classification - the Ubiquitous Challenge, volume 29 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 640-647, Berlin Heidelberg, 2005. Springer.
  • Y. Jin. A comprehensive survey of fitness approximation in evolutionary computation. Soft Computing, vol. 9 no. 1, pages 3-12, 2005.
  • Y. Jin and Jürgen Branke. Evolutionary optimization in uncertain environments - A survey. IEEE Transactions on Evolutionary Computation, vol. 9 no. 3, pages 303-318, 2005.
  • A. Konnert. Method Comparison Studies Between Different Standardization Networks. In Proceedings of the AMCTM 2005, 2005.
  • R. Kopiez, C. Weihs, U. Ligges and J.I. Lee. In Search of Variables Distinguishing Low and High Achievers in a Music Sight Reading Task. In C. Weihs and W. Gaul, editors, Classification - the Ubiquitous Challenge, volume 29 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 593-599, Berlin Heidelberg, 2005. Springer.
  • Renato de Leone. Parallel algorithm for support vector machines training and quadratic optimization. Optimization Methods and Software,, vol. 20 no. 2 &3, pages 379-388, 2005.
  • U. Ligges. Programmieren mit R. Springer, Berlin Heidelberg, 2005.
  • U. Ligges and D. Murdoch. R Help Desk: Make CMDWork under Windows - an Example. R News, vol. 5 no. 2, pages 27-28, 2005.
  • J. Loughrey and P. Cunningham. Using Early-Stopping to Avoid Overfitting in Wrapper-Based Feature Selection Employing Stochastic Search. Technical report, , Department of Computer Science, Trinity College Dublin, Dublin, Ireland, 2005.
  • J. Loughrey and P. Cunningham. Overfitting in Wrapper-Based Feature Subset Selection: The Harder You Try the Worse it Gets. In Research and Development in Intelligent Systems XXI, pages 33-43, 2005. [DOI]
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  • K. Luebke and C. Weihs. Improving Feature Extraction by Replacing the Fisher Criterion by an Upper Error Bound. Pattern Recognition, vol. 38 no. 11, pages 2220-2223, 2005.
  • K. Luebke and C. Weihs. Improving Feature Extraction by Replacing the Fisher Criterion by an Upper Error Bound. Technical report, , 2005.
  • Vivien Marquard. Quantile regression and its application to dose-response curves. , 2005.
  • A. Messaoud, W. Theis, C. Weihs and F. Hering. Application and use of multivariate control charts in a BTA deep hole drilling process. In C. Weihs and W. Gaul, editors, Classification - the Ubiquitous Challenge, volume 29 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 648-655, Berlin Heidelberg, 2005. Springer. [DOI]
  • A. Messaoud, C. Weihs and F. Hering. Time Series, Control Charts: An Industrial Application. In J. Janssen and P. Lenca, editors, Proceedings of the XIth International ASMDA 2005 Conference, pages 1329-1337, Brest, France, 2005.
  • F. Mörchen, A. Ultsch, M. Nöcker and C. Stamm. Databionic visualization of music collections according to perceptual distance. In In Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR 2005), pages 396-403, 2005.
  • A.M. Molinaro, R. Simon and R.M. Pfeiffer. Prediction error estimation: a comparison of resampling methods. Bioinformatics, vol. 21 no. 15, pages 3301-3307, 2005.
  • Wei Ping. Datentransformation zur Verbesserung der Klassifikation der Konjunkturphasen. , 2005.
  • C. Pumplün, S. Rüping, K. Morik and C. Weihs. D-Optimal Plans in Observational Studies. Technical report, , 2005.
  • C. Pumplün, C. Weihs and A. Preusser. Experimental Design for Variable Selection in Data Bases. In C. Weihs and W. Gaul, editors, Classification - the Ubiquitous Challenge, volume 29 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 192-199, Berlin Heidelberg, 2005. Springer.
  • Nils Raabe, Karsten Luebke and Claus Weihs. KMC/EDAM: A New Approach for the Visualization of K-Means Clustering Results. In Claus Weihs and Wolfgang Gaul, editors, Classification - the Ubiquitous Challenge, volume 29 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 200-207, Berlin Heidelberg, 2005. Springer.
  • Nils Raabe, O. Webber, Winfried Theis and Claus Weihs. Spiralling in BTA-deep-hole-drilling-models of varying frequencies. Technical report, , 2005.
  • C. Röver, F. Klefenz and C. Weihs. Identification of Musical Instruments by Means of the Hough-Transformation. In C. Weihs and W. Gaul, editors, Classification - the Ubiquitous Challenge, volume 29 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 608-615, Berlin Heidelberg, 2005. Springer.
  • C. Röver and G. Szepannek. Application of a Genetic Algorithm to Variable Selection in Fuzzy Clustering. In C. Weihs and W. Gaul, editors, Classification - the Ubiquitous Challenge, volume 29 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 624-631, Berlin Heidelberg, 2005. Springer.
  • Ilinca Schmitt. Logic regression in diagnostic classification problems. , 2005.
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  • Dirk Surmann. Statistische Modellierung von Wäscheunwuchten in Waschautomaten. , 2005.
  • G. Szepannek, F. Klefenz and C. Weihs. Neuronale Repräsentation des Hörvorgangs als Basis zur Schallanalyse. Informatikspektrum, vol. 28 no. 5, pages 389-395, 2005.
  • G. Szepannek and K. Luebke. Different Subspace Classification. In C. Weihs and W. Gaul, editors, Classification - the Ubiquitous Challenge, volume 29 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 224-231, Berlin Heidelberg, 2005. Springer.
  • G. Szepannek, K. Luebke and C. Weihs. Understanding Patterns with Different Subspace Classification. In P. Perner and A. Imiya, editors, Machine Learning and Data Mining in Pattern Recognition, volume 3587 of Lecture Notes in Artificial Intelligence, pages 110-119, Berlin Heidelberg, 2005. Springer.
  • G. Szepannek and C. Weihs. Variable Selection for Discrimination of More Than Two Classes Where Data are Sparse. Technical report, , 2005.
  • R. Telmoudi. A multi-stream process capability assessment using a nonconformity ratio based desirability function. , 2005.
  • W. Theis and C. Weihs. Determination of Relevant Frequencies and Modeling Varying Amplitudes of Harmonic Processes. In Weihs C. and Gaul W.(Eds), editors, Classification - The Ubiquitous Challenge, volume 29 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 656-663, Berlin Heidelberg, 2005. Springer.
  • Tomislav Smuc Goran Topic. Reimplementation of the Random Forest Algorithm. Parallel Numerics, pages 119-125, 2005.
  • H. Trautmann and J. Mehnen. A method for including a-priori-preferences in multicriteria optimization. Technical report, , 2005.
  • I.W. Tsang, J.T. Kwok, P.-ming Cheung and N. Cristianini. Core vector machines: Fast SVM training on very large data sets. Journal of Machine Learning Research, vol. 6, pages 363-392, 2005.
  • G. Tutz and H. Binder. Localized Classification. Statistics and Computing, vol. 15, pages 155-166, 2005.
  • S. Viaenea and G: Guido Dedenea. Cost-sensitive learning and decision making revisited. European Journal of Operational Research, vol. 166 no. 1, pages 212-220, 2005.
  • C. Weihs and W. Gaul, editors. Classification - the Ubiquitous Challenge, volume 29 of Studies in Classification, Data Analysis, and Knowledge Organization, Berlin Heidelberg, 2005. Springer.
  • C. Weihs and U. Ligges. From Local to Global Analysis of Music Time Series. In K. Morik, J.F. Boulicaut and A. Siebes, editors, Local Pattern Detection, volume 3539 of Lecture Notes in Artificial Intelligence, pages 233-245, Berlin Heidelberg, 2005. Springer.
  • C. Weihs and U. Ligges. Parameter Optimization in Automatic Transcription of Music. Technical report, , 2005.
  • C. Weihs, U. Ligges, K. Luebke and N. Raabe. klaR Analyzing German Business Cycles. In D. Baier, R. Decker and L. Schmidt Thieme, editors, Data Analysis and Decision Support, volume 30 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 335-343, Berlin Heidelberg, 2005. Springer.
  • C. Weihs, C. Reuter and U. Ligges. Register Classification by Timbre. In C. Weihs and W. Gaul, editors, Classification - the Ubiquitous Challenge, volume 29 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 624-631, Berlin Heidelberg, 2005. Springer.
  • C. Weihs, G. Szepannek, U. Ligges, K. Luebke and N. Raabe. Local Models in Register Classification by Timbre. Technical report, , 2005.
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2004

  • R. Akbani, S. Kwek and N. Japkowicz. Applying support vector machines to imbalanced datasets. In In Proceedings of the 15th European Conference on Machine Learning (ECML), pages 39-50, 2004.
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  • F.R. Bach, G.R.G. Lanckriet and M.I. Jordan. Fast Kernel Learning using Sequential Minimal Optimization. Technical report, , 2004.
  • Y. Bengio and Y. Grandvalet. No Unbiased Estimator of the Variance of K-Fold Cross-Validation. Journal of Machine Learning Research, vol. 5, pages 1089-1105, 2004.
  • L. Bottou. Advanced Lectures on Machine Learning, volume 3176 of Lecture Notes in Artificial Intelligence, chapter Stochastic Learning, pages 146-168. Springer Verlag, Berlin, 2004.
  • R.R. Bouckaert and E. Frank. Evaluating the Replicability of Significance Tests for Comparing Learning Algorithms. In Lecture Notes in Computer Science, volume 3056, pages 3-12, Berlin Heidelberg, 2004. Springer. [DOI]
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  • C. Chen, A. Liaw and L. Breiman. Using Random Forest to Learn Imbalanced Data. , 2004.
  • S. Chiaretti, X. Li, R. Gentleman, A. Vitale, M. Vignetti, F. Mandelli, J. Ritz and R. Foa. Gene expression profile of adult T-cell acute lymphocytic leukemia identifies distinct subsets of patients with different response to therapy and survival. Blood, vol. 103 no. 7, pages 2771-2778, 2004.
  • G.O. Consortium. The Gene Ontology (GO) database and informatics resource. Nucleic Acids Research, vol. 32 (Database Issue), pages 258-261, 2004.
  • T. Fowcett. ROC Graphs: Notes and Practical Considerations for Researchers. , pages 1-38, 2004.
  • U. Garczarek and C. Weihs. Incorporating Background Knowledge for Better Prediction of Cycle Phases. Knowledge and Information Systems, vol. 6, pages 544-569, 2004.
  • U. Genschel and C. Becker. Schließende Statistik. Springer-Verlag, Berlin, 2004.
  • A. Konnert and C. Berding. The Statistical Basis of Standardization Designs for Diagnostic Assays. Accreditation and Quality Assurance, vol. 9 no. 8, pages 457-463, 2004.
  • R. Kopiez, C. Weihs, U. Ligges and J.I. Lee. In Search of Variables Distinguishing Low and High Achievers in Music Sight Reading Task. Technical report, , 2004.
  • K. Luebke, I. Czogiel and C. Weihs. Latent Factor Prediction Pursuit for Rank Deficient Regressors. Technical report, , 2004.
  • K. Luebke, I. Czogiel and C. Weihs. A Note on the Dimension of the Projection Space in a Latent Factor Regression Model with Application to Business Cycle Classification. Technical report, , 2004.
  • K. Luebke, I. Czogiel and C. Weihs. A Computer Intensive Method for Choosing the Ridge Parameter. Technical report, , 2004.
  • K. Luebke and C. Weihs. Optimal Separation Projection. In J. Antoch, editors, COMPSTAT 2004 - Proceedings in Computational Statistics, pages 1429-1437, Heidelberg, 2004. Physica-Verlag.
  • K. Luebke and C. Weihs. Generation of Prediction Optimal Projection on Latent Factors by a Stochastic Search Algorithm. Computational Statistics and Data Analysis, vol. 47 no. 2, pages 297-310, 2004.
  • J. Menke and T.R. Martinez. Using Permutations Instead of Studentt Distribution for p-values in Paired-Difference Algorithm Comparisons. In Proceedings of the 2004 IEEE Joint Conference on Neural Networks IJCNN, 2004.
  • A. Messaoud, W. Theis, C. Weihs and F. Hering. Improving the BTA-Deep-Hole Drilling Process Using Multivariate Control Charts. In S. Ekinovic, S. Brdarevic, J. Vivancos and F. Puerta, editors, Proceedings of the 8th International Research/Expert Conference in the Development of Machinery and Associated TechnologyTMT 2004, pages 67-70, Neum, Bosnia and Herzegovina, 2004.
  • A. Messaoud, W. Theis, C. Weihs and F. Hering. Monitoring of the BTA Deep Hole Drilling Process Using Residual Control Charts. Technical report, , 2004.
  • A. Messaoud, C. Weihs and F. Hering. A Nonparametric Multivariate Control Chart Based on Data Depth. Technical report, , 2004.
  • H.T Nguyen and A. Smeulders. Active learning using Pre-clustering. In Proceedings of the twenty-first international conference on Machine learning. ACM, 2004.
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  • A. Passerini, M. Pontil and P. Frasconi. New results on error correcting output codes of kernel machines. IEEE Transactions on Neural Networks, vol. 15, pages 45-54, 2004.
  • G. Rötter and U. Ligges. Die Beeinflußbarkeit emotionalen Erlebens von Musik durch olfaktorische Reize. In K.E. Behne, G. Kleinen and H. de la Motte-Haber, editors, Musikpsychologie, volume 17 of Jahrbuch der Deutschen Gesellschaft für Musikpsychologie, pages 126-136, Göttingen, 2004. Hogrefe.
  • C. Röver and G. Szepannek. Application of a Genetic Algorithm to Variable Selection in Fuzzy Clustering. Technical report, , 2004.
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  • W. Theis. Modelling Varying Amplitudes. , 2004.
  • H. Trautmann. Qualitätskontrolle in der Industrie anhand von Kontrollkarten für Wünschbarkeitsindizes - Anwendungsfeld Lagerverwaltung. , 2004.
  • H. Trautmann. The Desirability Index as an Instrument for Multivariate Process Control. Technical report, , 2004.
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  • P. Voglewede. Parabola approximation for peak determination. Global DSP Magazine, vol. 3 no. 5, pages 13-17, 2004.
  • C. Weihs and U. Ligges. Interfaces in statistischen Anwendungssystemen: Die Entwicklung der letzten 25 Jahre aus persönlicher Sicht. In R. Biehler, J. Engel and J. Meyer, editors, Neue Medien und innermathematische Vernetzung in der Stochastik: Anregungen zum Stochastikunterricht, volume 2, pages 127-150, Hildesheim, 2004. Verlag Franzbecker.
  • C. Weihs and U. Ligges. From Local to Global Analysis of Music Time Series. Technical report, , 2004.
  • C. Weihs, U. Ligges and U. Garczarek. Prediction of Notes from Vocal Time Series: An Overview. In D. Baier and K.D. Wernecke, editors, Innovations in Classification, Data Science, and Information Systems, volume 27 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 283-294, Berlin Heidelberg, 2004. Springer.
  • C. Weihs, C. Reuter and U. Ligges. Register Classification by Timbre. Technical report, , 2004.

2003

  • Susanne Balzer. Vorhersage des Verlustes von Isopropyl Alkohol und Dichlormethan aus Tanks der chemischen Industrie. , 2003.
  • P.L. Bartlett, M.I. Jordan and J.D. Mcauliffe. Convexity, classification, and risk bounds. Technical report, , 2003.
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  • A.M. Busse. Klassifikation von Datenreihen mit Hilfe des Lyapunov-Exponenten. , 2003.
  • A. Christmann and C. Weihs, editors. Data Mining und Statistik in Hochschule und Wirtschaft. Proceedings der 6. Konferenz der SAS-Anwender in Forschung und Entwicklung (KSFE). Shaker Verlag, Aachen, 2003.
  • K. Duan, S.S. Keerthi and A.N. Poo. Evaluation of simple performance measures for tuning SVM hyperparameters. Neurocomputing, vol. 51, pages 41-59, 2003.
  • U. Garczarek and C. Weihs. Standardizing the Comparison of Partitions. Computational Statistics, vol. 18, pages 143-162, 2003.
  • U. Garczarek, C. Weihs and U. Ligges. Prediction of Notes from Vocal Time Series Produced by Singing Voice. Technical report, , 2003.
  • Sonja Görke. Charakterisierung von deutsch gesungenen Vokalen und Konsonanten durch Spektren. , 2003.
  • M. Goto, H. Hashiguchi, T. Nishimura and R. Oka. RWC Music Database: Music Genre Database and Musical Instrument Sound Database. In , pages 229-230, 2003.
  • I. Guyon and A. Elisseeff. An introduction to Variable and Feature Selection. The Journal of Machine Learning Research, vol. 3, pages 1157-1182, 2003.
  • D. J. Hand and V. Vinciotti. Local Versus Global Models for Classification Problems: Fitting Models Where it Matters. The American Statistician, vol. 57 no. 2, pages 124-131, 2003.
  • T. Hothorn. Bundling classifiers with an application to glaucoma diagnosis. , 2003.
  • C.W. Hsu, C.C. Chang and C.J. Lin. A practical guide to support vector classification. Technical report, , Taipei, 2003.
  • Shigeo Abe Yoshiaki Koshiba. Comparison of L1 and L2 support vector machines. Proc. of the International Joint Conference on Neural Networks, vol. 3, pages 2054 - 2059, 2003.
  • U. Ligges. R Help Desk: Package Management. R News, vol. 3 no. 3, pages 37-39, 2003.
  • U. Ligges. R-WinEdt. In Technische Universität Wien, K. Hornik, F. Leisch and A. Zeileis, editors, Proceedings of the 3rd International Workshop on Distributed Statistical Computing, March 20-22, Vienna, 2003.
  • U. Ligges. R Help Desk: Getting Help - RHelp Facilities and Manuals. R News, vol. 3 no. 1, pages 26-28, 2003.
  • U. Ligges and M. Mächler. Scatterplot3d - an R Package for Visualizing Multivariate Data. Journal of Statistical Software, vol. 8 no. 11, pages 1-20, 2003.
  • K. Luebke and C. Weihs. Prediction Optimal Data Analysis by Means of Stochastic Search. In M. Schader, W. Gaul and M. Vichi, editors, Between Data Science and Applied Data Analysis, volume 24 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 305-312, Berlin Heidelberg, 2003. Springer.
  • K. Luebke and C. Weihs. Testing a Simulated Annealing Algorithm in a Classification Problem. In A. Albrecht and K. Steinhoefel, editors, Stochastic Algorithms: Foundations and Applications, volume 2827 of Lecture Notes in Computer Science, pages 61-70, Berlin Heidelberg, 2003. Springer.
  • M.A. Maloof. Learning When Data Sets are Imbalanced and When Costs are Unequal and Unknown. Workshop on Learning from Imbalanced Data Sets, vol. 2, 2003.
  • D. Meyer, F. Leisch and K. Hornik. The support vector machine under test. Neurocomputing, vol. 55 no. 1-2, pages 169-186, 2003.
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2002

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2001

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  • X. Huang, A. Acero and H.-W. Hon. Spoken Language Processing: A Guide to Theory, Algorithm and System Development. Prentice Hall PTR, 2001.
  • J. Jessenberger and C. Weihs. A Note on the Behaviour of the Process Capability Index Cwith Asymmetric Specification Limits. Journal of Quality Technology, vol. 32, pages 438-441, 2001.
  • Melanie John. Der Einfluss der Stichprobenaufteilung auf die Erstellung von Scoretabellen. , 2001.
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  • Britta Pouwels. Diskriminanzanalyse bei fast-singulären Kovarianzmatrizen. , 2001.
  • I. Steinwart. On the Influence of the Kernel on the Consistency of Support Vector Machines. Journal of Machine Learning Research, vol. 2, pages 67-93, 2001.
  • D. Stemann and C. Weihs. The EWMA-X-S-Control Chart and its Performance in the Case of Precise and Imprecise Data. Statistical Papers, vol. 42, pages 207-223, 2001.
  • S. Sundararajan and S.S. Keerthi. Predictive Approaches for Choosing Hyperparameters in Gaussian Processes. Neural Computation, vol. 13 no. 5, pages 1103-1118, 2001.
  • C. Weihs, S. Berghoff, P. Hasse Becker and U. Ligges. Assessment of Purity of Intonation in Singing Presentations by Discriminant Analysis. In J. Kunert and G. Trenkler, editors, Mathematical Statistics and Biometrical Applications, pages 395-410, Lohmar, 2001. Josef Eul Verlag.
  • C. Weihs and J. Kunert. Greedy Variable Selection in Experimental Studies. In Workshop Learning, Database Sampling, Experimental Design: Views on Instance SelectionECML/PKDD 01, pages 6-20, Freiburg, 2001.
  • C. Weihs and U. Sondhauß. Incorporating background knowledge for better prediction of cycle phases. In R. Klinkenberg, S. Rüping, A. Fick, N. Henze, C. Herzog, R. Molitor and O. Schröder, editors, LLWA 01 - Tagungsband der GI-Workshop-Woche Lernen - Lehren - Wissen - Adaptivität, pages 27-34, Dortmund, 2001.
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2000

  • E.L. Allwein, R.E. Schapire and Y. Singer. Reducing multiclass to binary: A unifying approach for margin classifiers. Journal of Machine Learning Research, vol. 1, pages 113-141, 2000.
  • E.D. Andersen and K.D. Andersen. The MOSEK interior point optimizer for linear programming: An implementation of the homogeneous algorithm. In H. Frenk, K. Roos, T. Terlaky and S. Zhang, editors, High Performance Optimization, Dordrecht/Boston/New York, 2000. Kluwer Academic Publishers.
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  • C.K. Charles, C. Taylor and J. Keller. Meta-Analysis: From Data Characterisation for Meta-Learning to Meta-Regression. In Proceedings of the PKDD-00 Workshop on Data Mining, Decision Support,Meta-Learning and ILP, 2000.
  • K. Crammer. On the learnability and design of output codes for multiclass problems. In In Proceedings of the Thirteenth Annual Conference on Computational Learning Theory, pages 35-46, 2000.
  • S. Ben David, N. Eiron and P.M. Long. On the Difficulty of Approximately Maximizing Agreements. Journal of Computer and System Sciences, vol. 66, pages 266-274, 2000.
  • N.A. Diamantidis, D. Karlis and E.A. Giakoumakis. Unsupervised stratification of cross-validation for accuracy estimation. Artificial Intelligence, vol. 116 no. 1-2, pages 1-16, 2000.
  • R. Jin, W. Chen and T.W. Simpson. Comparative Studies Of Metamodeling Techniques Under Multiple Modeling Criteria. Structural and Multidisciplinary Optimization, vol. 23, pages 1-13, 2000.
  • Y. Jin, M. Olhofer and B. Sendhoff. On evolutionary optimization with approximate fitness functions. In L. D. Whitley and others, editors, Genetic and Evolutionary Computation Conference (GECCO), pages 786-793. Morgan Kaufmann, 2000.
  • A. Kalousis and M. Hilario. Model selection via meta-learning: a comparative study. In ICTAI Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence, pages 406-413, Washington, DC, USA, 2000. IEEE Computer Society.
  • U. Ligges. Identifikation lokal stationärer Anteile in Gesangszeitreihen. , 2000.
  • B. Pfahringer, H. Bensusan and C. Giraud Carrier. Meta-learning by landmarking various learning algorithms. In Proceedings of the Seventeenth International Conference on Machine Learning, ICML, pages 743-750. Morgan Kaufmann, 2000.
  • D.G. Saari and V.R. Merlin. A Geometric Examination Of KemenyRule. Social Choice and Welfare, vol. 17 no. 3, pages 403-438, 2000.
  • W. Theis and C. Weihs. Clustering techniques for the detection of business cycles. In R. Decker and W. (Eds) Gaul, editors, Classification and Information Processing at the Turn of the Millennium, volume 16 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 127-134, Berlin Heidelberg, 2000. Springer.
  • K.M. Ting. An Empirical Study of MetaCost Using Boosting Algorithms. In ECML Proceedings of the 11th European Conference on Machine Learning, volume 1810, pages 413-425, Berlin Heidelberg, 2000. Springer. [DOI]
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  • C. Weihs and U. Sondhauss. Business phase classification and prediction: How to compare interpretability of classification methods?. In H.H. Hoos and T.G. (Eds) Stützle, editors, Proceedings of the ECAI Workshop Notes Methods in Artificial Intelligence, pages 65-77, 2000.
  • Claus Weihs and Ullrich Heilemann. Taschenbuch der Statistik, chapter Diskriminanzanalyse, pages 583-608. Fachbuchverlag, Leipzig, 2000.
  • E. Zitzler, K. Deb and L. Thiele. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation, vol. 8 no. 2, pages 173-195, 2000.

1999

  • E. Alpaydin. Combined 5x2cv F test for comparing supervised classification learning algorithms. Neural Computation, vol. 11, pages 1885-1892, 1999.
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  • Martina Erdbrügge. Näherung und Prognose latenter Qualitätsmerkmale. , 1999.
  • Alexander Finke. Ein Vergleich von rekurrenten neuronalen Netzen und statistischer Zeitreihenmodellierung. , 1999.
  • Thomas Finke. Prognose multivariater Qualitätsmerkmale in der Kunststoffverarbeitung. , 1999.
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  • T.R. Golub, D.K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J.P. Mesirov, H. Coller, M.L. Loh, J.R. Downing, M.A. Caligiuri, C.D. Bloomfield and E.S. Lander. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, vol. 286 no. 5439, pages 531-537, 1999.
  • Gerhard S. Hellemann. The random permutation test as an alternative to the chi square test for comparing covariance structure models across groups. , 1999.
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  • Marc Hohmann. Versuchsplanung beim Hochgeschwindigkeitsfräsen. , 1999.
  • Jutta Jessenberger. Prozessfähigkeitsindizes in der Qualitätssicherung. BoD, Norderstedt, 1999.
  • T. Joachims. Making large-Scale SVM Learning Practical. Advances in Kernel Methods - Support Vector Learning, 1999.
  • B.S. John, J.C. Platt, J. Shawe taylor, A.J. Smola and R.C. Williamson. Estimating the Support of a High-Dimensional Distribution. Neural Computation, vol. 13, page 2001, 1999.
  • P.J. Kootsookos. A Review of the Frequency Estimation and Tracking Problems. Technical report, , 1999.
  • M. Kreutz, A.M. Reimetz, B. Sendhoff, C. Weihs and W. von Seelen. Structure Optimization of Density Estimation Models Applied to Regression with Dynamic Noise. In D. Heckerman and J. Whittaker, editors, Uncertainty 99: Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, pages 237-242, San Francisco, 1999. Morgan Kaufmann.
  • A. Lyhyaoui, M. Martinez, I. Mora, M. Vaquez, J.L. Sancho and A.R. Figueiras Vidal. Sample selection via clustering to construct support vector-like classifiers. IEEE Trans Neural Netw., vol. 10 no. 6, pages 1474-1481, 1999.
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1998

  • Bernd Bolzenius. Analyse von Kontrollkarten bei abhängigen Beobachtungen. , 1998.
  • C.J.C. Burges. A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, vol. 2, pages 121-167, 1998.
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1997

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1996

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1995

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1994

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1993

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1992

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1991

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1990

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1989

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1988

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1987

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1986

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1985

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1984

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1983

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1982

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1980

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1979

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1977

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1974

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1973

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1972

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1963

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1961

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1951

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1950

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1936

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