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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| 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.
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| Scholz/Klinkenberg/2006b |
Scholz, Martin and Klinkenberg, Ralf.
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| Mierswa/2006a |
Mierswa, Ingo.
Evolutionary Learning with Kernels: A Generic Solution for Large Margin Problems.
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| Mierswa/Wurst/2006a |
Mierswa, Ingo and Wurst, Michael.
Information Preserving Multi-Objective Feature Selection for Unsupervised Learning.
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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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|
| 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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2005.
|
| Mierswa/Morik/2005a |
Mierswa, Ingo and Morik, Katharina.
Automatic Feature Extraction for Classifying Audio Data.
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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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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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| Mierswa/Wurst/2005c |
Mierswa, Ingo and Wurst, Michael.
Efficient Case Based Feature Construction for Heterogeneous Learning Tasks.
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| Morik/2005a |
Morik, Katharina.
Informatik Spektrum, Themenheft Musik.
2005.
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| 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.
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| Rueping/2005c |
Rüping, Stefan.
Learning with Local Models.
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| Rueping/Scheffer/2005a |
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Proceedings of the ICML 2005 Workshop on Learning with Multiple Views.
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| Scholz/2005b |
Scholz, Martin.
Sampling-Based Sequential Subgroup Mining.
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Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '05),
pages 265--274,
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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,
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2005.
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| Scholz/2005d |
Scholz, Martin.
On the Tractability of Rule Discovery from Distributed Data.
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| Scholz/2005e |
Scholz, Martin.
On the Complexity of Rule Discovery from Distributed Data.
No. 31,
SFB475, Universitat Dortmund,
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| Scholz/Klinkenberg/2005a |
Scholz, Martin and Klinkenberg, Ralf.
An Ensemble Classifier for Drifting Concepts.
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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,
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| Mierswa/2004b |
Mierswa, Ingo.
Automatic Feature Extraction from Large Time Series.
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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.
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| Klinkenberg/Rueping/2003a |
Klinkenberg, Ralf and Rüping, Stefan.
Concept Drift and the Importance of Examples.
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2003.
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| Rueping/Morik/2003a |
Rüping, Stefan and Morik, Katharina.
Support Vector Machines and Learning about Time.
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| Rueping/Morik/2003b |
Rüping, Stefan and Morik, Katharina.
Support Vector Machines and Learning about Time.
No. 4,
SFB475, Universitat Dortmund,
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2003.
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| Joachims/2002b |
Joachims, Thorsten.
Learning to Classify Text using Support Vector Machines.
Vol. 668,
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2002.
|
| Morik/2002a |
Morik, Katharina.
Detecting Interesting Instances.
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| 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.
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| Morik/Rueping/2002a |
Morik, Katharina and Rüping, Stefan.
A Multistrategy Approach to the Classification of Phases in Business Cycles.
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| Rueping/2002a |
Stefan Rüping.
Support Vector Machines in Relational Databases.
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| Rueping/2002c |
Rüping, Stefan.
Incremental Learning with Support Vector Machines.
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SFB475, Universitat Dortmund,
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| Klinkenberg/etal/2001a |
Klinkenberg, Ralf and Ruping, Stefan and Fick, Andreas and Henze, Nicola and Herzog, Christian and Molitor, Ralf and Schroder, Olaf (editors).
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Rüping, Stefan.
SVM Kernels for Time Series Analysis.
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| Rueping/2001b |
Rüping, Stefan.
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| Sondhauss/Weihs/2001a |
Sondhauss, Ursula and Weihs, Claus.
Incorporating background knowledge for better prediction of cycle phases.
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Universitat Dortmund,
2001.
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| Joachims/00a |
Joachims, Thorsten.
Estimating the Generalization Performance of a SVM Efficiently.
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| Klinkenberg/Joachims/2000a |
Klinkenberg, Ralf and Joachims, Thorsten.
Detecting Concept Drift with Support Vector Machines.
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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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| 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.
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| 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.
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| Brockhausen/99b |
Peter Brockhausen.
Learning First--Order Rules in Intensive Care Monitoring.
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Leipzig,
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| Joachims/99a |
Joachims, Thorsten.
Making large-Scale SVM Learning Practical.
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T. Joachims.
Estimating the Generalization Performance of a SVM Efficiently.
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Universitat Dortmund, LS VIII,
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T. Joachims and A. McCallum and M. Sahami and M. Craven (editors).
Machine Learning for Information Filtering.
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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.
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Tobias Scheffer and Thorsten Joachims.
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| Brockhausen/Morik/98a |
Brockhausen, Peter and Morik, Katharina.
Wissensentdeckung in relationalen Datenbanken: Eine Herausforderung für das maschinelle Lernen.
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Joachims, Thorsten.
Text Categorization with Support Vector Machines: Learning with Many Relevant Features.
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| Joachims/98c |
Thorsten Joachims.
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1998.
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M. Sahami and M. Craven and T. Joachims and A. McCallum (editors).
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| Scheffer/Joachims/98a |
Tobias Scheffer and Thorsten Joachims.
Estimating the expected error of empirical minimizers for model selection.
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TU-Berlin,
1998.
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| Imhoff/etal/97a |
Michael Imhoff and Markus Bauer and Ursula Gather and D. Lohlein.
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T. Joachims.
Text Categorization with Support Vector Machines: Learning with Many Relevant Features.
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| Morik/97c |
Morik, Katharina.
Knowledge Discovery in Databases -- An Inductive Logic Programming Approach.
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| Morik/Brockhausen/97a |
Morik, Katharina and Brockhausen, Peter.
A Multistrategy Approach to Relational Knowledge Discovery in Databases.
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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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F. Wiechers.
Verwaltung grosser Datenmengen fur die effiziente Anwendung des Apriori-Algorithmus zur Wissensentdeckung in Datenbanken.
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Morik, Katharina and Brockhausen, Peter.
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