Algorithmic Learning Theory: 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004. Proceedings / Edition 1

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This book constitutes the refereed proceedings of the 15th International Conference on Algorithmic Learning Theory, ALT 2004, held in Padova, Italy in October 2004.

The 29 revised full papers presented together with 5 invited papers and 3 tutorial summaries were carefully reviewed and selected from 91 submissions. The papers are organized in topical sections on inductive inference, PAC learning and boosting, statistical supervised learning, online sequence learning, approximate optimization algorithms, logic based learning, and query and reinforcement learning.

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Table of Contents

Invited Papers.- String Pattern Discovery.- Applications of Regularized Least Squares to Classification Problems.- Probabilistic Inductive Logic Programming.- Hidden Markov Modelling Techniques for Haplotype Analysis.- Learning, Logic, and Probability: A Unified View.- Regular Contributions.- Learning Languages from Positive Data and Negative Counterexamples.- Inductive Inference of Term Rewriting Systems from Positive Data.- On the Data Consumption Benefits of Accepting Increased Uncertainty.- Comparison of Query Learning and Gold-Style Learning in Dependence of the Hypothesis Space.- Learning r-of-k Functions by Boosting.- Boosting Based on Divide and Merge.- Learning Boolean Functions in AC 0 on Attribute and Classification Noise.- Decision Trees: More Theoretical Justification for Practical Algorithms.- Application of Classical Nonparametric Predictors to Learning Conditionally I.I.D. Data.- Complexity of Pattern Classes and Lipschitz Property.- On Kernels, Margins, and Low-Dimensional Mappings.- Estimation of the Data Region Using Extreme-Value Distributions.- Maximum Entropy Principle in Non-ordered Setting.- Universal Convergence of Semimeasures on Individual Random Sequences.- A Criterion for the Existence of Predictive Complexity for Binary Games.- Full Information Game with Gains and Losses.- Prediction with Expert Advice by Following the Perturbed Leader for General Weights.- On the Convergence Speed of MDL Predictions for Bernoulli Sequences.- Relative Loss Bounds and Polynomial-Time Predictions for the k-lms-net Algorithm.- On the Complexity of Working Set Selection.- Convergence of a Generalized Gradient Selection Approach for the Decomposition Method.- Newton Diagram and Shastic Complexity in Mixture of Binomial Distributions.- Learnability of Relatively Quantified Generalized Formulas.- Learning Languages Generated by Elementary Formal Systems and Its Application to SH Languages.- New Revision Algorithms.- The Subsumption Lattice and Query Learning.- Learning of Ordered Tree Languages with Height-Bounded Variables Using Queries.- Learning Tree Languages from Positive Examples and Membership Queries.- Learning Content Sequencing in an Educational Environment According to Student Needs.- Tutorial Papers.- Statistical Learning in Digital Wireless Communications.- A BP-Based Algorithm for Performing Bayesian Inference in Large Perceptron-Type Networks.- Approximate Inference in Probabilistic Models.

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