| An Axiomatic Approach to Feature Term Generalization | p. 1 |
| Lazy Induction of Descriptions for Relational Case-Based Learning | p. 13 |
| Estimating the Predictive Accuracy of a Classifier | p. 25 |
| Improving the Robustness and Encoding Complexity of Behavioural Clones | p. 37 |
| A Framework for Learning Rules from Multiple Instance Data | p. 49 |
| Wrapping Web Information Providers by Transducer Induction | p. 61 |
| Learning While Exploring: Bridging the Gaps in the Eligibility Traces | p. 73 |
| A Reinforcement Learning Algorithm Applied to Simplified Two-Player Texas Hold'em Poker | p. 85 |
| Speeding Up Relational Reinforcement Learning through the Use of an Incremental First Order Decision Tree Learner | p. 97 |
| Analysis of the Performance of AdaBoost.M2 for the Simulated Digit-Recognition-Example | p. 109 |
| Iterative Double Clustering for Unsupervised and Semi-supervised Learning | p. 121 |
| On the Practice of Branching Program Boosting | p. 133 |
| A Simple Approach to Ordinal Classification | p. 145 |
| Fitness Distance Correlation of Neural Network Error Surfaces: A Scalable, Continuous Optimization Problem | p. 157 |
| Extraction of Recurrent Patterns from Stratified Ordered Trees | p. 167 |
| Understanding Probabilistic Classifiers | p. 179 |
| Efficiently Determining the Starting Sample Size for Progressive Sampling | p. 192 |
| Using Subclasses to Improve Classification Learning | p. 203 |
| Learning What People (Don't) Want | p. 214 |
| Towards a Universal Theory of Artificial Intelligence Based on Algorithmic Probability and Sequential Decisions | p. 226 |
| Convergence and Error Bounds for Universal Prediction of Nonbinary Sequences | p. 239 |
| Consensus Decision Trees: Using Consensus Hierarchical Clustering for Data Relabelling and Reduction | p. 251 |
| Learning of Variability for Invariant Statistical Pattern Recognition | p. 263 |
| The Evaluation of Predictive Learners: Some Theoretical and Empirical Results | p. 276 |
| An Evolutionary Algorithm for Cost-Sensitive Decision Rule Learning | p. 288 |
| A Mixture Approach to Novelty Detection Using Training Data with Outliers | p. 300 |
| Applying the Bayesian Evidence Framework to v-Support Vector Regression | p. 312 |
| DQL: A New Updating Strategy for Reinforcement Learning Based on Q-Learning | p. 324 |
| A Language-Based Similarity Measure | p. 336 |
| Backpropagation in Decision Trees for Regression | p. 348 |
| Comparing the Bayes and Typicalness Frameworks | p. 360 |
| Symbolic Discriminant Analysis for Mining Gene Expression Patterns | p. 372 |
| Social Agents Playing a Periodical Policy | p. 382 |
| Learning When to Collaborate among Learning Agents | p. 394 |
| Building Committees by Clustering Models Based on Pairwise Similarity Values | p. 406 |
| Second Order Features for Maximising Text Classification Performance | p. 419 |
| Importance Sampling Techniques in Neural Detector Training | p. 431 |
| Induction of Qualitative Trees | p. 442 |
| Text Categorization Using Transductive Boosting | p. 454 |
| Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing | p. 466 |
| Using Domain Knowledge on Population Dynamics Modeling for Equation Discovery | p. 478 |
| Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL | p. 491 |
| A Unified Framework for Evaluation Metrics in Classification Using Decision Trees | p. 503 |
| Improving Term Extraction by System Combination Using Boosting | p. 515 |
| Classification on Data with Biased Class Distribution | p. 527 |
| Discovering Admissible Simultaneous Equation Models from Observed Data | p. 539 |
| Discovering Strong Principles of Expressive Music Performance with the PLCG Rule Learning Strategy | p. 552 |
| Proportional k-Interval Discretization for Naive-Bayes Classifiers | p. 564 |
| Using Diversity in Preparing Ensembles of Classifiers Based on Different Feature Subsets to Minimize Generalization Error | p. 576 |
| Geometric Properties of Naive Bayes in Nominal Domains | p. 588 |
| Support Vectors for Reinforcement Learning | p. 600 |
| Combining Discrete Algorithmic and Probabilistic Approaches in Data Mining | p. 601 |
| Statistification or Mystification? The Need for Statistical Thought in Visual Data Mining | p. 602 |
| The Musical Expression Project: A Challenge for Machine Learning and Knowledge Discovery | p. 603 |
| Scalability, Search, and Sampling: From Smart Algorithms to Active Discovery | p. 615 |
| Author Index | p. 617 |
| Table of Contents provided by Blackwell. All Rights Reserved. |
