Front matter
1-12
An Axiomatic Approach to Feature Term Generalization
Hassan Aït-Kaci and Yutaka Sasaki
13-24
Lazy Induction of Descriptions for Relational Case-Based Learning
Eva Armengol and Enric Plaza
25-36
Estimating the Predictive Accuracy of a Classifier
Hilan Bensusan and Alexandros Kalousis
37-48
Improving the Robustness and Encoding Complexity of Behavioural Clones
Rui Camacho and Pavel Brazdil
49-60
A Framework for Learning Rules from Multiple Instance Data
Yann Chevaleyre and Jean-Daniel Zucker
61-72
Wrapping Web Information Providers by Transducer Induction
Boris Chidlovskii
73-84
Learning While Exploring: Bridging the Gaps in the Eligibility Traces
Fredrik A. Dahl and Ole Martin Halck
85-96
A Reinforcement Learning Algorithm Applied to Simplified Two-Player Texas Hold’em Poker
Fredrik A. Dahl
97-108
Speeding Up Relational Reinforcement Learning through the Use of an Incremental First Order Decision Tree Learner
Kurt Driessens, Jan Ramon and Hendrik Blockeel
109-120
Analysis of the Performance of AdaBoost.M2 for the Simulated Digit-Recognition-Example
Günther Eibl and Karl Peter Pfeiffer
121-132
Iterative Double Clustering for Unsupervised and Semi-supervised Learning
Ran El-Yaniv and Oren Souroujon
133-144
On the Practice of Branching Program Boosting
Tapio Elomaa and Matti Kääriäinen
145-156
A Simple Approach to Ordinal Classification
Eibe Frank and Mark Hall
157-166
Fitness Distance Correlation of Neural Network Error Surfaces: A Scalable, Continuous Optimization Problem
Marcus Gallagher
167-178
Extraction of Recurrent Patterns from Stratified Ordered Trees
Jean-Gabriel Ganascia
179-191
Understanding Probabilistic Classifiers
Ashutosh Garg and Dan Roth
192-202
Efficiently Determining the Starting Sample Size for Progressive Sampling
Baohua Gu, Bing Liu, Feifang Hu and Huan Liu
203-213
Using Subclasses to Improve Classification Learning
Achim Hoffmann, Rex Kwok and Paul Compton
214-225
Learning What People (Don’t) Want
Thomas Hofmann
226-238
Towards a Universal Theory of Artificial Intelligence Based on Algorithmic Probability and Sequential Decisions
Marcus Hutter
239-250
Convergence and Error Bounds for Universal Prediction of Nonbinary Sequences
Marcus Hutter
251-262
Consensus Decision Trees: Using Consensus Hierarchical Clustering for Data Relabelling and Reduction
Branko Kavšek, Nada Lavrač and Anuška Ferligoj
263-275
Learning of Variability for Invariant Statistical Pattern Recognition
Daniel Keysers, Wolfgang Macherey, Jörg Dahmen and Hermann Ney
276-287
The Evaluation of Predictive Learners: Some Theoretical and Empirical Results
Kevin B. Korb, Lucas R. Hope and Michelle J. Hughes
288-299
An Evolutionary Algorithm for Cost-Sensitive Decision Rule Learning
Wojciech Kwedlo and Marek Krętowski
300-311
A Mixture Approach to Novelty Detection Using Training Data with Outliers
Martin Lauer
312-323
Applying the Bayesian Evidence Framework to ν-Support Vector Regression
Martin H. Law and James T. Kwok
324-335
DQL: A New Updating Strategy for Reinforcement Learning Based on Q-Learning
Carlos E. Mariano and Eduardo F. Morales
336-347
A Language-Based Similarity Measure
Lionel Martin and Frédéric Moal
348-359
Backpropagation in Decision Trees for Regression
Victor Medina-Chico, Alberto Suárez and James F. Lutsko
360-371
Comparing the Bayes and Typicalness Frameworks
Thomas Melluish, Craig Saunders, Ilia Nouretdinov and Volodya Vovk
372-381
Symbolic Discriminant Analysis for Mining Gene Expression Patterns
Jason H. Moore, Joel S. Parker and Lance W. Hahn
382-393
Social Agents Playing a Periodical Policy
Ann Nowé, Johan Parent and Katja Verbeeck
394-405
Learning When to Collaborate among Learning Agents
Santiago Ontañón and Enric Plaza
406-418
Building Committees by Clustering Models Based on Pairwise Similarity Values
Thomas Ragg
419-430
Second Order Features for Maximising Text Classification Performance
Bhavani Raskutti, Herman Ferrá and Adam Kowalczyk
431-441
Importance Sampling Techniques in Neural Detector Training
José L. Sanz-González and Diego Andina
442-453
Induction of Qualitative Trees
1Dorian Šuc and Ivan Bratko
454-465
Text Categorization Using Transductive Boosting
Hirotoshi Taira and Masahiko Haruno
466-477
Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing
Lappoon R. Tang and Raymond J. Mooney
478-490
Using Domain Knowledge on Population Dynamics Modeling for Equation Discovery
1Ljupčo Todorovski and Sašo DŽeroski
491-502
Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL
Peter D. Turney
503-514
A Unified Framework for Evaluation Metrics in Classification Using Decision Trees
Ricardo Vilalta, Mark Brodie, Daniel Oblinger and Irina Rish
515-526
Improving Term Extraction by System Combination Using Boosting
Jordi Vivaldi, 2Lluís Màrquez and Horacio Rodríguez
527-538
Classification on Data with Biased Class Distribution
Slobodan Vucetic and Zoran Obradovic
539-551
Discovering Admissible Simultaneous Equation Models from Observed Data
Takashi Washio, Hiroshi Motoda and Niwa Yuji
552-563
Discovering Strong Principles of Expressive Music Performance with the PLCG Rule Learning Strategy
Gerhard Widmer
564-575
Proportional k-Interval Discretization for Naive-Bayes Classifiers
Ying Yang and Geoffrey I. Webb
576-587
Using Diversity in Preparing Ensembles of Classifiers Based on Different Feature Subsets to Minimize Generalization Error
Gabriele Zenobi and Pádraig Cunningham
587-599
Geometric Properties of Naive Bayes in Nominal Domains
Huajie Zhang and Charles X. Ling
600
Support Vectors for Reinforcement Learning
Thomas G. Dietterich and Xin Wang
601
Combining Discrete Algorithmic and Probabilistic Approaches in Data Mining
Heikki Mannila
602
Statistification or Mystification? The Need for Statistical Thought in Visual Data Mining
Antony Unwin
603-614
The Musical Expression Project: A Challenge for Machine Learning and Knowledge Discovery
Gerhard Widmer
615
Scalability, Search, and Sampling: From Smart Algorithms to Active Discovery
Stefan Wrobel
Back matter