13. ECML 2001: Freiburg, Germany

Regular Papers

• Hassan Aït-Kaci, Yutaka Sasaki:
  An Axiomatic Approach to Feature Term Generalization. 1-12
  
• Eva Armengol, Enric Plaza:
  Lazy Induction of Descriptions for Relational Case-Based Learning. 13-24
  
• Hilan Bensusan, Alexandros Kalousis:
  Estimating the Predictive Accuracy of a Classifier. 25-36
  
• Rui Camacho, Pavel Brazdil:
  Improving the Robustness and Encoding Complexity of Behavioural Clones. 37-48
  
• Yann Chevaleyre, Jean-Daniel Zucker:
  A Framework for Learning Rules from Multiple Instance Data. 49-60
  
• Boris Chidlovskii:
  Wrapping Web Information Providers by Transducer Induction. 61-72
  
• Fredrik A. Dahl, Ole Martin Halck:
  Learning While Exploring: Bridging the Gaps in the Eligibility Traces. 73-84
  
• Fredrik A. Dahl:
  A Reinforcement Learning Algorithm Applied to Simplified Two-Player Texas Hold'em Poker. 85-96
  
• Kurt Driessens, Jan Ramon, Hendrik Blockeel:
  Speeding Up Relational Reinforcement Learning through the Use of an Incremental First Order Decision Tree Learner. 97-108
  
• Günther Eibl, Karl Peter Pfeiffer:
  Analysis of the Performance of AdaBoost.M2 for the Simulated Digit-Recognition-Example. 109-120
  
• Ran El-Yaniv, Oren Souroujon:
  Iterative Double Clustering for Unsupervised and Semi-supervised Learning. 121-132
  
• Tapio Elomaa, Matti Kääriäinen:
  On the Practice of Branching Program Boosting. 133-144
  
• Eibe Frank, Mark Hall:
  A Simple Approach to Ordinal Classification. 145-156
  
• Marcus Gallagher:
  Fitness Distance Correlation of Neural Network Error Surfaces: A Scalable, Continuous Optimization Problem. 157-166
  
• Jean-Gabriel Ganascia:
  Extraction of Recurrent Patterns from Stratified Ordered Trees. 167-178
  
• Ashutosh Garg, Dan Roth:
  Understanding Probabilistic Classifiers. 179-191
  
• Baohua Gu, Bing Liu, Feifang Hu, Huan Liu:
  Efficiently Determining the Starting Sample Size for Progressive Sampling. 192-202
  
• Achim G. Hoffmann, Rex Bing Hung Kwok, Paul Compton:
  Using Subclasses to Improve Classification Learning. 203-213
  
• Thomas Hofmann:
  Learning What People (Don't) Want. 214-225
  
• Marcus Hutter:
  Towards a Universal Theory of Artificial Intelligence Based on Algorithmic Probability and Sequential Decisions. 226-238
  
• Marcus Hutter:
  Convergence and Error Bounds for Universal Prediction of Nonbinary Sequences. 239-250
  
• Branko Kavsek, Nada Lavrac, Anuska Ferligoj:
  Consensus Decision Trees: Using Consensus Hierarchical Clustering for Data Relabelling and Reduction. 251-262
  
• Daniel Keysers, Wolfgang Macherey, Jörg Dahmen, Hermann Ney:
  Learning of Variability for Invariant Statistical Pattern Recognition. 263-275
  
• Kevin B. Korb, Lucas R. Hope, Michelle J. Hughes:
  The Evaluation of Predictive Learners: Some Theoretical and Empirical Results. 276-287
  
• Wojciech Kwedlo, Marek Kretowski:
  An Evolutionary Algorithm for Cost-Sensitive Decision Rule Learning. 288-299
  
• Martin Lauer:
  A Mixture Approach to Novelty Detection Using Training Data with Outliers. 300-311
  
• Martin H. C. Law, James T. Kwok:
  Applying the Bayesian Evidence Framework to \nu -Support Vector Regression. 312-323
  
• Carlos Eduardo Mariano, Eduardo F. Morales:
  DQL: A New Updating Strategy for Reinforcement Learning Based on Q-Learning. 324-335
  
• Lionel Martin, Frédéric Moal:
  A Language-Based Similarity Measure. 336-347
  
• Victor Medina-Chico, Alberto Suárez, James F. Lutsko:
  Backpropagation in Decision Trees for Regression. 348-359
  
• Thomas Melluish, Craig Saunders, Ilia Nouretdinov, Volodya Vovk:
  Comparing the Bayes and Typicalness Frameworks. 360-371
  
• Jason H. Moore, Joel S. Parker, Lance W. Hahn:
  Symbolic Discriminant Analysis for Mining Gene Expression Patterns. 372-381
  
• Ann Nowé, Johan Parent, Katja Verbeeck:
  Social Agents Playing a Periodical Policy. 382-393
  
• Santiago Ontañón, Enric Plaza:
  Learning When to Collaborate among Learning Agents. 394-405
  
• Thomas Ragg:
  Building Committees by Clustering Models Based on Pairwise Similarity Values. 406-418
  
• Bhavani Raskutti, Herman L. Ferrá, Adam Kowalczyk:
  Second Order Features for Maximising Text Classification Performance. 419-430
  
• José L. Sanz-González, Diego Andina:
  Importance Sampling Techniques in Neural Detector Training. 431-441
  
• Dorian Suc, Ivan Bratko:
  Induction of Qualitative Trees. 442-453
  
• Hirotoshi Taira, Masahiko Haruno:
  Text Categorization Using Transductive Boosting. 454-465
  
• Lappoon R. Tang, Raymond J. Mooney:
  Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing. 466-477
  
• Ljupco Todorovski, Saso Dzeroski:
  Using Domain Knowledge on Population Dynamics Modeling for Equation Discovery. 478-490
  
• Peter D. Turney:
  Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL. 491-502
  
• Ricardo Vilalta, Mark Brodie, Daniel Oblinger, Irina Rish:
  A Unified Framework for Evaluation Metrics in Classification Using Decision Trees. 503-514
  
• Jordi Vivaldi, Lluís Màrquez, Horacio Rodríguez:
  Improving Term Extraction by System Combination Using Boosting. 515-526
  
• Slobodan Vucetic, Zoran Obradovic:
  Classification on Data with Biased Class Distribution. 527-538
  
• Takashi Washio, Hiroshi Motoda, Yuji Niwa:
  Discovering Admissible Simultaneous Equation Models from Observed Data. 539-551
  
• Gerhard Widmer:
  Discovering Strong Principles of Expressive Music Performance with the PLCG Rule Learning Strategy. 552-563
  
• Ying Yang, Geoffrey I. Webb:
  Proportional k-Interval Discretization for Naive-Bayes Classifiers. 564-575
  
• Gabriele Zenobi, Padraig Cunningham:
  Using Diversity in Preparing Ensembles of Classifiers Based on Different Feature Subsets to Minimize Generalization Error. 576-587
  
• Huajie Zhang, Charles X. Ling:
  Geometric Properties of Naive Bayes in Nominal Domains. 588-599
  

Invited Papers

• Thomas G. Dietterich, Xin Wang:
  Support Vectors for Reinforcement Learning. 600
  
• Heikki Mannila:
  Combining Discrete Algorithmic and Probabilistic Approaches in Data Mining. 601
  
• Antony Unwin:
  Statistification or Mystification? The Need for Statistical Thought in Visual Data Mining. 602
  
• Gerhard Widmer:
  The Musical Expression Project: A Challenge for Machine Learning and Knowledge Discovery. 603-614
  
• Stefan Wrobel:
  Scalability, Search, and Sampling: From Smart Algorithms to Active Discovery. 615