13. ECML 2001: Freiburg, Germany

Luc De Raedt, Peter A. Flach (Eds.): Machine Learning: EMCL 2001, 12th European Conference on Machine Learning, Freiburg, Germany, September 5-7, 2001, Proceedings. Lecture Notes in Computer Science 2167 Springer 2001, ISBN 3-540-42536-5 BibTeX

@proceedings{DBLP:conf/ecml/2001,
  editor    = {Luc De Raedt and
               Peter A. Flach},
  title     = {Machine Learning: EMCL 2001, 12th European Conference on Machine
               Learning, Freiburg, Germany, September 5-7, 2001, Proceedings},
  booktitle = {ECML},
  publisher = {Springer},
  series    = {Lecture Notes in Computer Science},
  volume    = {2167},
  year      = {2001},
  isbn      = {3-540-42536-5},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Regular Papers

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

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