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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.

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. Computing on high performance clusters with R: Packages BatchJobs and BatchExperiments. Technical report, , 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 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.
  • C. Weihs. Statistics for hearing aids: Auralization. In Second Bilateral German-Polish Symposium on Data Analysis and its Applications (GPSDAA), 2011.

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.
  • 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.
  • 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.
  • 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.
  • 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]
  • 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.
  • 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. On the Distribution of EMOA Hypervolumes. In Learning and Intelligent Optimization, volume 6073 of Lecture Notes in Computer Science, pages 333-337. Springer, 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.
  • 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.
  • 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. 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.
  • 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.
  • 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 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.

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.
  • B. Bischl, U. Ligges and C. Weihs. Frequency estimation by DFT interpolation: A comparison of methods. Technical report, , 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.
  • K. Hornik and D. Meyer. relations: Data Structures and Algorithms for Relations. 2009.
  • O. Mersmann. emoa: Evolutionary Multiobjective Optimization Algorithms. 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.
  • 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.
  • J. Schiffner and C. Weihs. D-optimal plans for variable selection in data bases. Technical report, , 2009.
  • 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.
  • 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.

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. 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.
  • I. Jahnke, M. Tzankow, A. van Veen and C. Weihs. Hochschulforschung und Hochschulmanagement im Dialog - Zur Praxisrelevanz empirischer Forschung über die Hochschule, chapter Informationsaustausch und Wissensmanagement in Online-Communities - Neue Kommunikationsräume an der Universität, pages 119-136. Waxmann, 2008.
  • J. Kunert and C. Weihs. Editorial: Seventh Annual ENBIS Conference. Quality and Reliability Engineering International, vol. 24 no. 6, page 625, 2008. [DOI]
  • J. Mehnen and H. Trautmann. Robust Multi-objective Optimisation of Weld Bead Geometry for Additive Manufacturing. In R. Teti, editors, Proceedings of the 6th CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME, pages 419-424, Naples, Italy, 2008. Copyright C.O.C. Com. org. Conv..
  • A. Messaoud and C. Weihs. On the Properties of the Rank Based Multivariate Exponentially Weighted Moving Average Control Charts. 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 455-462, Berlin Heidelberg, 2008. Springer.
  • A. Messaoud, C. Weihs and F. Hering. Detection of chatter vibration in a drilling process using multivariate control charts. Computational Statistics & Data Analysis, vol. 52 no. 6, pages 3208-3219, 2008.
  • N. Raabe and C. Weihs. Universitäten als Landschaften. Das Hochschulwesen, vol. 3, pages 80-84, 2008.
  • J. Schiffner and C. Weihs. Comparison of local classification methods. 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 69-76, Berlin Heidelberg, 2008. Springer.
  • K. Sommer and C. Weihs. A comparative study on polyphonic musical time series using MCMC methods. 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 285-292. Springer, 2008.
  • G. Szepannek, B. Bischl and C. Weihs. On the Combination of Locally Optimal Pairwise Classifiers. Journal of Engineering Applications of Artificial Intelligence, vol. 22 no. 1, pages 79-85, 2008.
  • G. Szepannek, J. Schiffner, J. Wilson and C. Weihs. Local Modelling in Classification. In P. Perner, editors, Advances in Data Mining. Medical Applications, E-Commerce, Marketing, and Theoretical Aspects, volume 5077/2008 of Lecture Notes in Computer Science, pages 153-164, Berlin Heidelberg, 2008. Springer.
  • M. Thoene and C. Weihs. Vielseitig und gefragt: Absolventinnen und Absolventen des Dortmunder Studiengangs Statistik. AStA Wirtschafts- und Sozialstatistisches Archiv, vol. 2 no. 1-2, pages 75-92, 2008.
  • H. Trautmann, U. Ligges, J. Mehnen and M. Preuss. A Convergence Criterion for Multiobjective Evolutionary Algorithms Based on Systematic Statistical Testing. In G. Rudolph and others, editors, Parallel Problem Solving from Nature (PPSN), pages 825-836, Berlin Heidelberg, 2008. Springer.
  • C. Weihs. Statistical Inference, Econometric Analysis and Matrix Algebra. Festschrift in Honour of Götz Trenkler, chapter Testing Numerical Methods Solving the Linear Least Squares Problem, pages 333-347. Springer, 2008.

2007

  • C. Becker and W. Theiß. Classification and Clustering in Business Cycle Analysis, volume 79 of RWI-Schriften, chapter Combining dimension reduction and fuzzy clustering: An application to business cycles, pages 137-144. Duncker & Humblot, Berlin, 2007.
  • I. Czogiel, K. Luebke, M. Zentgraf and C. Weihs. Localized linear discriminant analysis. In R. Decker and H.J. Lenz, editors, Advances in Data Analysis, volume 34 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 133-140, Berlin Heidelberg, 2007. Springer.
  • U. Garczarek and C. Weihs. Klassifikations-/Clustermethoden und Konjunkturanalyse, volume 79 of RWI-Schriften, chapter Univariate Characterization of the German Business Cycle 1955 - 1994, pages 127-136. Duncker & Humblot, Berlin, 2007.
  • M. Gebel and C. Weihs. Calibrating Classifier Scores into Probabilities. In GfKl, R. Decker and H.J. Lenz, editors, Advances in Data Analysis, volume 34 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 141-148, Berlin Heidelberg, 2007. Springer.
  • T. Harczos, W. Nogueira, G. Szepannek and F. Klefenz. Comparative Evaluation of Successive Cochlear Modelling Stages as Possible Front-Ends for Automatic Speech Recognition. In A. Calvo Manzano and S. Santiago, editors, Proceedings of the 19th International Congress on Acoustics (ICA), Madrid, 2007.
  • T. Harczos, G. Szepannek and F. Klefenz. Towards Automatic Speech Recognition Based on Cochlear Travelling Wave Delay Trajectories. In Proc. ISAAR Conference, Helsingoer, 2007.
  • U. Heilemann and C. Weihs, editors. Classification and Clustering in Business Cycle Analysis, volume 79 of RWI-Schriften. Duncker & Humblot, Berlin, 2007.
  • G. Kauermann and C. Weihs. Editorial: Statistical Consulting. Advances in Statistical Analysis (AStA), vol. 91 no. 4, pages 343-347, 2007.
  • U. Ligges. Programmieren mit R. Springer, Berlin Heidelberg, 2007.
  • J. Mehnen, H. Trautmann and A. Tiwari. Introducing User Preference Using Desirability Functions in Multi-Objective Evolutionary Optimisation of Noisy Processes. In J.-X.X. Kay Chen Tan, editors, CEC 2007 - IEEE Congress on Evolutionary Computation, pages 2687-2694, Singapore, 2007. IEEE Catalog Number: 07TH8963C, Library of Congress: 2007928155.
  • J. Mehnen, H. Trautmann and A. Tiwari. Introducing User Preference Using Desirability Functions in Multi-Objective Evolutionary Optimisation of Noisy Processes. In K.C. Tan and J.-X. Xu, editors, CEC 2007 - IEEE Congress on Evolutionary Computation, pages 2687-2694, Singapore, 2007. IEEE Catalog Number: 07TH8963C, ISBN: 1-4244-1340-0, Library of Congress: 2007928155.
  • K. Sommer and C. Weihs. Using MCMC as a Stochastic Optimization Procedure for Monophonic and Polyphonic Sound. In R. Decker and H.J. Lenz, editors, Advances in Data Analysis, volume 34 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 645-652, Berlin Heidelberg, 2007. Springer.
  • G. Szepannek, B. Bischl and C. Weihs. On the Combination of Locally Optimal Pairwise Classifiers. In Machine Learning and Data Mining in Pattern Recognition, volume 4571 of Lecture Notes in Computer Science, pages 104-116, Berlin Heidelberg, 2007. Springer.
  • 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.
  • C. Weihs. Quality assurance for statistical consulting. Advances in Statistical Analysis, vol. 91 no. 4, pages 429-440, 2007.
  • C. Weihs and U. Garczarek. Klassifikations-/Clustermethoden und Konjunkturanalyse, volume 79 of RWI-Schriften, chapter Stability of Multivariate Representation of Business Cycles over Time, pages 55-68. Duncker & Humblot, Berlin, 2007.
  • C. Weihs, U. Ligges, F. Mörchen and D. Müllensiefen. Classification in Music Research. Advances in Data Analysis and Classification (ADAC), vol. 1 no. 3, pages 255-291, 2007.
  • C. Weihs and H. Trautmann. Parallel Universes: Multi-Criteria Optimization. In M.R. Berthold, K. Morik and A. Siebes, editors, Dagstuhl Seminar Proceedings 07181, Parallel Universes and Local Patterns, Schloss Dagstuhl, Germany, 2007.
  • Claus Weihs, Nils Raabe and O. Webber. Deriving a statistical model for the prediction of spiralling in BTA-deep-hole drilling from a physical model. Technical report, , 2007.
  • K. Weinert, C. Weihs, O. Webber and N. Raabe. Varying bending eigenfrequencies in BTA deep hole drilling: mechanical modeling using statistical parameter estimation. Production Engineering: Research and Development, vol. 1 no. 2, pages 127-134, 2007.

2006

  • D. Enache, C. Weihs and U. Garczarek. Classification-relevant Importance Measures for the West German Business Cycle. 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 470-477, Berlin Heidelberg, 2006. Springer.
  • T. Harczos, G. Szepannek, A. Katai and F. Klefenz. An Auditory-Model Based Vowel Classification. In IEEE BioCAS Biomedical Systems and L. Circuits Conference, editors, Proceedings of IEEE BioCAS Biomedical Systems and Circuits Conference, London, 29.11. - 1.12.06, 2006.
  • U. Heilemann and C. Weihs, editors. Klassifikations-/Clustermethoden und Konjunkturanalyse, volume 79 of RWI-Schriften. Duncker & Humblot, Berlin, 2006.
  • R. Kopiez, C. Weihs, U. Ligges and J.I. Lee. Classification of high and low achievers in a music sight-reading task. Psychology of Music, vol. 34 no. 1, pages 5-26, 2006.
  • U. Ligges. Programmieren mit R (in Japanese). Springer (Japan), Tokyo, 2006.
  • U. Ligges. R Help Desk: Accessing the Sources. R News, vol. 6 no. 4, pages 43-45, 2006.
  • J. Mehnen and H. Trautmann. Integration of ExpertPreferences in Pareto Optimization by Desirability Function Techniques. In R. Teti, editors, CIRP ICME - Proceedings of the 5th CIRP International Seminar on Intelligent Computation in Manufacturing Engineering, pages 293-298, Ischia, Italy, 2006. C.O.C. Com. org. Conv. CIRP ICME.
  • A. Messaoud, C. Weihs and F. Hering. Nonlinear Time Series, Modelling: Monitoring a Drilling Process. 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 302-309, Berlin Heidelberg, 2006. Springer.
  • N. Raabe, O. Webber, D. Enk and C. Weihs. Physical models for the prediction and prevention of dynamic disturbances in BTA deep hole drilling. In Proceedings of ENBIS 2006, 2006.
  • N. Raabe, O. Webber, W. Theis and C. Weihs. Spiralling in BTA Deep-Hole Drilling: Models of Varying Frequencies. In From Data and Information Analysis to Knowledge Engineering, volume 31 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 510-517, Berlin Heidelberg, 2006. Springer.
  • K. Sommer and C. Weihs. Using MCMC as a Stochastic Optimization Procedure for Musical Time Series. 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 307-315, Berlin Heidelberg, 2006. Springer.
  • 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. 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. 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. 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.

2005

  • A. Christmann, K. Luebke, M. Marin Galiano and S. Rüping. Determination of Hyper-parameters for Kernel Based Classification and Regression. Technical report, , 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.
  • 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.
  • 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.
  • 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.
  • K. Luebke and C. Weihs. Improving Feature Extraction by Replacing the Fisher Criterion by an Upper Error Bound. Technical report, , 2005.
  • 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. Prediction Optimal Classification of Business Phases. Technical report, , 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.
  • 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.
  • S. Sturtz, U. Ligges and A. Gelman. R2WinBUGS: A Package for Running WinBUGS from R. Journal of Statistical Software, vol. 12 no. 3, pages 1-16, 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.
  • 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.
  • H. Trautmann and J. Mehnen. A method for including a-priori-preferences in multicriteria optimization. Technical report, , 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. Parameter Optimization in Automatic Transcription of Music. Technical report, , 2005.
  • 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, 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.

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.
  • 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. 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, I. Czogiel and C. Weihs. Latent Factor Prediction Pursuit for Rank Deficient Regressors. Technical report, , 2004.
  • 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.
  • 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.
  • 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, 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, C. Weihs and F. Hering. A Nonparametric Multivariate Control Chart Based on Data Depth. Technical report, , 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.
  • G. Szepannek and K. Luebke. Different Subspace Classification. Technical report, , 2004.
  • H. Trautmann. The Desirability Index as an Instrument for Multivariate Process Control. Technical report, , 2004.
  • H. Trautmann and C. Weihs. Uncertainty of the Optimum Influence Factor Levels in Multicriteria Optimization Using the Concept of Desirability. Technical report, , 2004.
  • H. Trautmann and C. Weihs. Pareto-Optimality and Desirability Indices. Technical report, , 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

  • 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.
  • 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.
  • 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. R Help Desk: Package Management. R News, vol. 3 no. 3, pages 37-39, 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. 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.
  • 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.
  • G. Szepannek, K. Luebke and C. Weihs. Gruppierung der Spielweisen von Vereinen in der Fussballbundesligasaison 2002/03 mit Hilfe von Clusteranalysen. Technical report, , 2003.
  • H. Trautmann and C. Weihs. On the Distribution of the Desirability Index Using HarringtonDesirability Function. Technical report, , 2003.
  • C. Weihs and U. Ligges. Automatic Transcription of Singing Performances. Technical report, , 2003.
  • C. Weihs and U. Ligges. Voice Prints as a Tool for Automatic Classification of Vocal Performance. In R. Kopiez, A.C. Lehmann, I. Wolther and C. Wolf, editors, Proceedings of the 5th Triennial ESCOM Conference, pages 332-335, Hanover University of Music and Drama, Germany, 2003.
  • C. Weihs and U. Ligges. Automatic Transcription of Singing Performances. In Bulletin of the International Statistical Institute, 54th Session, Proceedings, Volume LX, Book 2, pages 507-510, 2003.
  • C. Weihs, U. Ligges, J. Güttner, P. Hasse Becker and S. Berghoff. Classification and Clustering of Vocal Performances. 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 118-126, Berlin Heidelberg, 2003. Springer.

2002

  • J. Groß, K. Luebke and C. Weihs. A Note on the General Solution for a Projection Matrix in Latent Factor Models. Technical report, , 2002.
  • T. Hothorn, K. Hornik and U. Ligges. R: Eine Sprache für Datenanalyse und -visualisierung -- Das freie S. iX, vol. 3/2002, pages 141-143, 2002.
  • U. Ligges. R Help Desk: Automation of Mathematical Annotation in Plots. R News, vol. 2 no. 3, pages 32-34, 2002.
  • U. Ligges and M. Mächler. Scatterplot3d - an R Package for Visualizing Multivariate Data. Technical report, , 2002.
  • U. Ligges, C. Weihs and P. Hasse Becker. Detection of Locally Stationary Segments in Time Series - Algorithms and Applications. Technical report, , 2002.
  • U. Ligges, C. Weihs and P. Hasse Becker. Detection of Locally Stationary Segments in Time Series. In W. Härdle and B. Rönz, editors, COMPSTAT 2002 - Proceedings in Computational Statistics, pages 285-290, Heidelberg, 2002. Physica-Verlag.
  • K. Luebke and W. Theis. Checking the Validity of Chemotaxonomical Markers by Optimal Predictive Projections. Technical report, , 2002.
  • A. Preusser, U. Ligges and C. Weihs. Ein R Exportfilter für das Notations- und Midi-Programm LilyPond. Technical report, , 2002.
  • M. C. Röhl, C. Weihs and W. Theis. Direct Minimization of Error Rates in Multivariate Classification. Computational Statistics, vol. 17, pages 29-46, 2002.
  • U. Sondhauss and C. Weihs. Standardized Partition Spaces. In W. Härdle and B. Rönz, editors, COMPSTAT 2002 - Proceedings in Computational Statistics, pages 539-544, Heidelberg, 2002. Physica-Verlag.
  • C. Weihs and U. Sondhauß. Combining Mental Fit and Data Fit for Classification Rule Selection. In O. Opitz and M. (Eds) Schwaiger, editors, Explanatory Data Analysis in Empirical ResearchStudies in Classification, Data Analysis, and Knowledge Organization, pages 188-203. Springer, 2002.
  • Claus Weihs and M. Kappler. e-stat: Development of a Scenario for Statistics in Chemical Engineering. In W. Härdle and B. Rönz, editors, COMPSTAT 2002 - Proceedings in Computational Statistics, pages 327-332, Heidelberg, 2002. Physica-Verlag.

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.
  • 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.
  • 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.

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.
  • 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.

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.
  • M. C. Röhl and C. Weihs. Optimal vs. Classical Linear Dimension Reduction. In W. Gaul and H. (Eds) Locarek Junge, editors, Classification in the Information Age, volume 14 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 252-259, Berlin Heidelberg, 1999. Springer.
  • C. Weihs and J. Jessenberger. Statistische Methoden zur Qualitätssicherung und -optimierung in der Industrie. Wiley-VCH, Weinheim, 1999.

1998

  • M. Kreutz, A.M. Reimetz, B. Sendhoff, C. Weihs and W. von Seelen. Optimisation of Density Estimation Models with Evolutionary Algorithms. In A. Eiben, T. Bäck, M. Schoenauer and H. Schwefel, editors, Parallel Problem Solving from Nature (PPSN), volume 1489 of Lecture Notes in Computer Science, pages 998-1007, Berlin Heidelberg, 1998. Springer.

1997

  • G. Arminger, D. Enache and T. Bonne. Analyzing Credit Risk Data: A Comparison of Logistic Discrimination, Classification Tree Analysis and Feedforward Networks. Computational Statistics, vol. 12 no. 2, pages 293-310, 1997.

1996

  • G. Arminger and D. Enache. Statistical Models and Artificial Neuronal Networks. In H.H. Bock and W. Polasek, editors, Data Analysis and Information Systems, volume 7, pages 243-260, Berlin Heidelberg, 1996. Springer.
  • C. Weihs and W. Seewald. Computer-based design of experiments in industry. In H.-H. Bock and W. Polasek, editors, Data Analysis and Information Systems. Statistical and Conceptual Approaches, volume 7 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 272-288, Berlin Heidelberg, 1996. Springer.

1995

  • C. Weihs, M. Berres and Y.-L. Grize. Statistical design of experiments in industrial practice. Surveys on Mathematics for Industry, vol. 5, pages 49-75, 1995.

1994

  • H. Schmidli and C. Weihs. Evaporation Loss from Solvent Tanks. The Chemical Engineering Journal, vol. 55, pages 61-68, 1994.

1993

  • C. Weihs. Canonical Discriminant Analysis: Comparison of Resampling Methods and Convex-Hull Approximation. In O. Opitz, B. Lausen and R. (Eds) Klar, editors, Information and Classification - Concepts, Methods, and Applications, volume 3 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 225-238, Berlin Heidelberg, 1993. Springer.
  • C. Weihs, W. Baumeister and H. Schmidli. Classification Methods for Multivariate Quality Parameters. Journal of Chemometrics, vol. 7, pages 131-142, 1993.
  • Claus Weihs. Multivariate Exploratory Data Analysis and Graphics: A Tutorial. Journal of Chemometrics, vol. 7 no. 5, pages 305-340, 1993.

1992

  • C. Weihs. Methoden und Werkzeuge für die exploratorische Datenanalyse in den Biowissenschaften, chapter Vorhersagefähigkeit multivariater linearer Methoden : Simulation und Grafik (mit Diskussion), pages 111-127. Fischer, Stuttgart, 1992.

1991

  • A. Racine, C. Weihs and A.F.M. Smith. Estimation of Relative Potency with Sequential Dilution Errors in Radioimmunoassay. Biometrics, vol. 47, pages 1235-1246, 1991.
  • C. Weihs and H. Schmidli. Multivariate Exploratory Data Analysis in Chemical Industry. Mikrochimica Acta, vol. 2, pages 467-482, 1991.
  • S.M. Weiss and C.A. Kulikowski. Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1991.

1990

  • C. Weihs and H. Schmidli. OMEGA - Online Multivariate Exploratory Graphical Analysis: Routine search for structure (with discussion). Statistical Science, vol. 5, pages 175-226, 1990.
  • C. Weihs and H. Schmidli. OMEGA - Online Multivariate Exploratory Graphical Analysis: Routine search for structure (mit Diskussion). ; Statistical Science, pages 175-226, 1990.
  • C. Weihs and H. Schmidli. OMEGA - Online Multivariate Exploratory Graphical Analysis: Routine search for structure (mit Diskussion). ; Statistical Science, pages 175-226, 1990.

1987

  • G. Calzolari, L. Panattoni and C. Weihs. Computational efficiency of FIML-estimation. Journal of Econometrics, vol. 36, pages 299-310, 1987.
  • C. Weihs. Arbeiten zur Angewandten Statistik, volume 30, chapter Auswirkungen von Fehlern in den Daten auf Parameterschätzungen und Prognosen. Physica, Heidelberg, 1987.
  • C. Weihs, G. Calzolari and L. Panattoni. The behavior of trust-region methods in FIML-estimation. Computing, vol. 38, pages 89-100, 1987.

1986

  • A. Kirchen and C. Weihs. Ökonomische Prognose-, Entscheidungs- und Gleichgewichtsmodelle, chapter Das IAS-System Bonn : Ein interaktives Software-System für den ökonometrischen Modellbau, pages 123-132. WILEY-VCH, Weinheim, 1986.

1985

  • C. Weihs. Convergence of an algorithm for FIML-estimation in (non-)linear econometric models. Proceedings für das IX. Symposium über Operations Research 1984, pages 101-126, 1985.

  • U. Ligges. tuneR.