The aim of project A4 is to combine statistical methods and methods of machine learning in order to improve Knowledge Discovery in Databases (KDD). After the process of the knowledge discovery was examined as a whole in the last period, we focus on two important problems in the current period. These problems often occur in practice of knowledge discovery. Corresponding research promises a special synergy effect because of the combination of statistical methods and machine learning methods: analysis temporal phenomenons in the form of events and the application of experimental design. Additionally, emphasis of the project is placed on the applied analysis of real databases.
Mierswa/Morik/2008a |
Mierswa, Ingo and Morik, Katharina.
About the Non-Convex Optimization Problem Induced by Non-positive Semidefinite Kernel Learning.
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2008.
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Mierswa/2007b |
Mierswa, Ingo.
Finding all Local Models in Parallel: Multi-Objective SVM.
2007.
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Mierswa/2007c |
Mierswa, Ingo.
Regularization through Multi-Objective Optimization.
In
Klinkenberg, Ralf and Mierswa, Ingo and Hinneburg, Alexander and Posch, Stefan and Neumann, Steffen (editors),
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Scholz/Klinkenberg/2006b |
Scholz, Martin and Klinkenberg, Ralf.
Boosting Classifiers for Drifting Concepts.
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Intelligent Data Analysis (IDA), Special Issue on Knowledge Discovery from Data Streams,
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No. 1,
pages 3--28,
2007.
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Mierswa/2006a |
Mierswa, Ingo.
Evolutionary Learning with Kernels: A Generic Solution for Large Margin Problems.
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2006.
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Mierswa/Wurst/2006a |
Mierswa, Ingo and Wurst, Michael.
Information Preserving Multi-Objective Feature Selection for Unsupervised Learning.
In
Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos Coello Coello and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens (editors),
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2006.
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Mierswa/Wurst/2006b |
Mierswa, Ingo and Wurst, Michael.
Sound Multi-Objective Feature Space Transformation for Clustering.
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pages 330--337,
2006.
|
Homburg/etal/2005a |
Homburg, Helge and Mierswa,Ingo and Moller, Bulent and Morik, Katharina and Wurst, Michael.
A Benchmark Dataset for Audio Classification and Clustering.
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Joshua D. Reiss and Geraint A. Wiggins (editors),
Proc. of the International Symposium on Music Information Retrieval 2005,
pages 528--531,
London, UK,
Queen Mary University,
2005.
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Mierswa/Morik/2005a |
Mierswa, Ingo and Morik, Katharina.
Automatic Feature Extraction for Classifying Audio Data.
In
Machine Learning Journal,
Vol. 58,
pages 127--149,
2005.
|
Mierswa/Morik/2005b |
Mierswa, Ingo and Morik, Katharina.
Method trees: building blocks for self-organizable representations of value series: how to evolve representations for classifying audio data.
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ACM,
2005.
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Mierswa/Wurst/2005b |
Mierswa, Ingo and Wurst, Michael.
Efficient Feature Construction by Meta Learning -- Guiding the Search in Meta Hypothesis Space.
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2005.
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Mierswa/Wurst/2005c |
Mierswa, Ingo and Wurst, Michael.
Efficient Case Based Feature Construction for Heterogeneous Learning Tasks.
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Alipio Jorge and Luis Torgo and Pavel Brazdil and Rui Camacho and Joao Gama (editors),
Proceedings of the European Conference on Machine Learning (ECML 2005),
pages 641--648,
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2005.
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Morik/2005a |
Morik, Katharina.
Informatik Spektrum, Themenheft Musik.
2005.
|
Morik/etal/2005c |
Morik, Katharina and Boulicaut, Jean-François and Siebes, Arno.
Local Pattern Detection.
Vol. 3539,
Springer,
2005.
|
Morik/Koepcke/2005a |
Morik, Katharina and Köpcke, Hanna.
Features for Learning Local Patterns in Time-Stamped Data.
In
Katharina Morik and Jean-Francois Boulicaut and Arno Siebes (editors),
Local Pattern Detection: International Seminar, Dagstuhl Castle, Germany, April 12-16, 2004, Revised Selected Papers,
Vol. LNCS 3539,
pages 98--114,
Springer,
2005.
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Rueping/2005c |
Rüping, Stefan.
Learning with Local Models.
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pages 153-170,
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2005.
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Rueping/Scheffer/2005a |
Ruping, Stefan and Scheffer, Tobias (editors).
Proceedings of the ICML 2005 Workshop on Learning with Multiple Views.
2005.
|
Scholz/2005b |
Scholz, Martin.
Sampling-Based Sequential Subgroup Mining.
In
Grossman, R. L. and Bayardo, R. and Bennett, K. and Vaidya, J. (editors),
Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '05),
pages 265--274,
Chicago, Illinois, USA,
ACM Press,
2005.
|
Scholz/2005c |
Scholz, Martin.
Comparing Knowledge-Based Sampling to Boosting.
No. 26,
Collaborative Research Center on the Reduction of Complexity for Multivariate Data Structures (SFB 475), University of Dortmund,
Dortmund, Germany,
2005.
|
Scholz/2005d |
Scholz, Martin.
On the Tractability of Rule Discovery from Distributed Data.
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Proceedings of the 5th IEEE International Conference on Data Mining (ICDM '05),
pages 761--764,
Houston, Texas, USA,
IEEE Computer Society,
2005.
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Scholz/2005e |
Scholz, Martin.
On the Complexity of Rule Discovery from Distributed Data.
No. 31,
SFB475, Universitat Dortmund,
Dortmund, Germany,
2005.
|
Scholz/Klinkenberg/2005a |
Scholz, Martin and Klinkenberg, Ralf.
An Ensemble Classifier for Drifting Concepts.
In
Gama, J. and Aguilar-Ruiz, J. S. (editors),
Proceedings of the Second International Workshop on Knowledge Discovery in Data Streams,
pages 53--64,
Porto, Portugal,
2005.
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Wurst/etal/2005a |
Wurst, Michael and Mierswa, Ingo and Morik, Katharina.
Structuring Music Collections by Exploiting Peers' Processing.
No. 43/05,
Collaborative Research Center 475, University of Dortmund,
2005.
|
Mierswa/2004b |
Mierswa, Ingo.
Automatic Feature Extraction from Large Time Series.
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Classification -- the Ubiquitous Challenge, Proc. of the 28. Annual Conference of the GfKl 2004,
pages 600--607,
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2004.
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Mierswa/Morik/2004a |
Mierswa, Ingo and Morik, Katharina.
Learning Feature Extraction for Learning from Audio Data.
No. 55/04,
Collaborative Research Center 475, University of Dortmund,
2004.
|
Morik/Koepcke/2004a |
Morik, Katharina and Köpcke, Hanna.
Analysing Customer Churn in Insurance Data - A Case Study.
In
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PKDD '04: Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases,
Vol. 3202,
pages 325--336,
New York, NY, USA,
Springer,
2004.
|
Klinkenberg/Rueping/2003a |
Klinkenberg, Ralf and Rüping, Stefan.
Concept Drift and the Importance of Examples.
In
Franke, Jurgen and Nakhaeizadeh, Gholamreza and Renz, Ingrid (editors),
Text Mining -- Theoretical Aspects and Applications,
pages 55--77,
Berlin, Germany,
Physica-Verlag,
2003.
|
Rueping/Morik/2003a |
Rüping, Stefan and Morik, Katharina.
Support Vector Machines and Learning about Time.
In
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'03),
2003.
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Rueping/Morik/2003b |
Rüping, Stefan and Morik, Katharina.
Support Vector Machines and Learning about Time.
No. 4,
SFB475, Universitat Dortmund,
Dortmund, Germany,
2003.
|
Joachims/2002b |
Joachims, Thorsten.
Learning to Classify Text using Support Vector Machines.
Vol. 668,
Kluwer,
2002.
|
Morik/2002a |
Morik, Katharina.
Detecting Interesting Instances.
In
Hand, David J. and Adams, Niall M. and Bolton, Richard J. (editors),
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery,
Vol. 2447,
pages 13-23,
Berlin,
Springer,
2002.
|
Morik/etal/2002a |
Morik, Katharina and Joachims, T. and Imhoff, M. and Brockhausen, P. and Rüping, S..
Integrating Kernel Methods into a Knowledge-based Approach to Evidence-based Medicine.
In
Schmitt, Manfred and Teodorescu, Horia-Nicolai and Jain, Ashlesha and Jain, Ajita and Jain, Sandhya and Jain, Lakhmi C. (editors),
Studies in Fuzzi- ness and Soft Computing.,
Vol. 96,
pages 71--99,
Physica-Verlag,
2002.
|
Morik/Rueping/2002a |
Morik, Katharina and Rüping, Stefan.
A Multistrategy Approach to the Classification of Phases in Business Cycles.
In
Elomaa, Taprio and Mannila, Heikki and Toivonen, Hannu (editors),
Machine Learning: ECML 2002,
Vol. 2430,
pages 307--318,
Berlin,
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2002.
|
Rueping/2002a |
Stefan Rüping.
Support Vector Machines in Relational Databases.
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Pattern Recognition with Support Vector Machines --- First International Workshop, SVM 2002,
pages 310--320,
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2002.
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Rueping/2002c |
Rüping, Stefan.
Incremental Learning with Support Vector Machines.
No. 18,
SFB475, Universitat Dortmund,
Dortmund, Germany,
2002.
|
Klinkenberg/etal/2001a |
Klinkenberg, Ralf and Ruping, Stefan and Fick, Andreas and Henze, Nicola and Herzog, Christian and Molitor, Ralf and Schroder, Olaf (editors).
LLWA 01 -- Tagungsband der GI-Workshop-Woche Lernen -- Lehren -- Wissen -- Adaptivitat.
No. Nr. 763,
Dortmund, Germany,
2001.
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Rueping/2001a |
Rüping, Stefan.
SVM Kernels for Time Series Analysis.
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LLWA 01 - Tagungsband der GI-Workshop-Woche Lernen - Lehren - Wissen - Adaptivitat,
pages 43-50,
Dortmund, Germany,
2001.
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Rueping/2001b |
Rüping, Stefan.
Incremental Learning with Support Vector Machines.
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Sondhauss/Weihs/2001a |
Sondhauss, Ursula and Weihs, Claus.
Incorporating background knowledge for better prediction of cycle phases.
No. 24,
Universitat Dortmund,
2001.
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Joachims/00a |
Joachims, Thorsten.
Estimating the Generalization Performance of a SVM Efficiently.
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Langley, Pat (editors),
Proceedings of the International Conference on Machine Learning,
pages 431--438,
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2000.
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Klinkenberg/Joachims/2000a |
Klinkenberg, Ralf and Joachims, Thorsten.
Detecting Concept Drift with Support Vector Machines.
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Proceedings of the Seventeenth International Conference on Machine Learning (ICML),
pages 487--494,
San Francisco, CA, USA,
Morgan Kaufmann,
2000.
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Morik/etal/2000a |
Morik, Katharina and Imhoff, Michael and Brockhausen, Peter and Joachims, Thorsten and Gather, Ursula.
Knowledge Discovery and Knowledge Validation in Intensive Care.
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2000.
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Arminger/Goetz/99a |
Arminger, Gerhard and Gotz, Norman.
Asymmetric Loss Functions for Evaluating the Quality of Forecasts in Time Series for Goods Management Systems.
No. 22,
Universitat Dortmund,
1999.
|
Arminger/Schneider/99a |
Arminger, Gerhard and Schneider, Carsten.
Frequent Problems of Model Specification and Forecasting of Time Series in Goods Management Systems.
No. 21,
Universitat Dortmund,
1999.
|
Brockhausen/99a |
Peter Brockhausen.
Learning First Order Rules in Intensive Care Monitoring.
In
Sa\vso D\vzeroski and Peter Flach (editors),
ILP--99 Late-Breaking Papers,
pages 22--27,
Bled, Slovenia,
1999.
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Brockhausen/99b |
Peter Brockhausen.
Learning First--Order Rules in Intensive Care Monitoring.
In
Petra Perner (editors),
Maschinelles Lernen, FGML 99,
pages 1--7,
Leipzig,
Institut fur Bildverarbeitung und angewandte Informatik,
1999.
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Joachims/99a |
Joachims, Thorsten.
Making large-Scale SVM Learning Practical.
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B. Schölkopf and C. Burges and A. Smola (editors),
Advances in Kernel Methods - Support Vector Learning,
Cambridge, MA,
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1999.
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Joachims/99e |
T. Joachims.
Estimating the Generalization Performance of a SVM Efficiently.
No. 25,
Universitat Dortmund, LS VIII,
1999.
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Joachims/etal/99a |
T. Joachims and A. McCallum and M. Sahami and M. Craven (editors).
Machine Learning for Information Filtering.
AAAI Press,
1999.
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Morik/etal/99a |
Morik, Katharina and Brockhausen, Peter and Joachims, Thorsten.
Combining statistical learning with a knowledge-based approach -- A case study in intensive care monitoring.
In
ICML '99: Proceedings of the Sixteenth International Conference on Machine Learning,
pages 268--277,
San Francisco, CA, USA,
Morgan Kaufmann Publishers Inc.,
1999.
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Scheffer/Joachims/99a |
Tobias Scheffer and Thorsten Joachims.
Expected Error Analysis for Model Selection.
In
International Conference on Machine Learning (ICML),
Bled, Slowenien,
1999.
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Brockhausen/Morik/98a |
Brockhausen, Peter and Morik, Katharina.
Wissensentdeckung in relationalen Datenbanken: Eine Herausforderung für das maschinelle Lernen.
In
Gholamreza Nakhaeizadeh (editors),
Data Mining, theoretische Aspekte und Anwendungen,
pages 193--211,
Physica Verlag,
1998.
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Joachims/98a |
Joachims, Thorsten.
Text Categorization with Support Vector Machines: Learning with Many Relevant Features.
In
Claire N\'edellec and C\'eline Rouveirol (editors),
Proceedings of the European Conference on Machine Learning,
pages 137 -- 142,
Berlin,
Springer,
1998.
|
Joachims/98c |
Thorsten Joachims.
Making large-Scale SVM Learning Practical.
No. 24,
Universitat Dortmund, LS VIII-Report,
1998.
|
Sahami/etal/98a |
M. Sahami and M. Craven and T. Joachims and A. McCallum (editors).
Learning for Text Categorization.
No. WS-98-05,
AAAI Press,
1998.
|
Scheffer/Joachims/98a |
Tobias Scheffer and Thorsten Joachims.
Estimating the expected error of empirical minimizers for model selection.
No. TR-98-9,
TU-Berlin,
1998.
|
Imhoff/etal/97a |
Michael Imhoff and Markus Bauer and Ursula Gather and D. Lohlein.
Time Series Analysis in Intensive Care Medicine.
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Vol. 6,
pages 203 -- 281,
1997.
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Joachims/97b |
T. Joachims.
Text Categorization with Support Vector Machines: Learning with Many Relevant Features.
No. 23,
Universitat Dortmund, LS VIII-Report,
1997.
|
Morik/97c |
Morik, Katharina.
Knowledge Discovery in Databases -- An Inductive Logic Programming Approach.
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Foundations of Computer Science -- Theory, Cognition, Applications,
pages 429--436,
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1997.
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Morik/Brockhausen/97a |
Morik, Katharina and Brockhausen, Peter.
A Multistrategy Approach to Relational Knowledge Discovery in Databases.
In
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Vol. 27,
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pages 287--312,
Kluwer,
1997.
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Morik/etal/97a |
Morik, Katharina and Pigeot, Iris and Robers, Ursula.
The Use of Inductive Logic Programming for the Development of the Statistical Software Tool CORA.
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München,
1997.
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Wiechers/97a |
F. Wiechers.
Verwaltung grosser Datenmengen fur die effiziente Anwendung des Apriori-Algorithmus zur Wissensentdeckung in Datenbanken.
Universitat Dortmund, Lehrstuhl 8,
1997.
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Morik/Brockhausen/96a |
Morik, Katharina and Brockhausen, Peter.
A Multistrategy Approach to Relational Knowledge Discovery in Databases.
In
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pages 17--27,
Palo Alto,
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1996.
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