Algorithmic Learning Theory: 10th International Conference, ALT’99 Tokyo, Japan, December 6–8, 1999 ProceedingsAuthor: Osamu Watanabe, Takashi Yokomori Published by Springer Berlin Heidelberg ISBN: 978-3-540-66748-3 DOI: 10.1007/3-540-46769-6Table of Contents:Tailoring Representations to Different Requirements Theoretical Views of Boosting and Applications Extended Stochastic Complexity and Minimax Relative Loss Analysis Algebraic Analysis for Singular Statistical Estimation Generalization Error of Linear Neural Networks in Unidentifiable Cases The Computational Limits to the Cognitive Power of the Neuroidal Tabula Rasa The Consistency Dimension and Distribution-Dependent Learning from Queries (Extended Abstract) The VC-Dimension of Subclasses of Pattern Languages On the V On the Strength of Incremental Learning Learning from Random Text Inductive Learning with Corroboration Flattening and Implication Induction of Logic Programs Based on ψ-Terms Complexity in the Case Against Accuracy: When Building One Function-Free Horn Clause Is as Hard as Any A Method of Similarity-Driven Knowledge Revision for Type Specializations PAC Learning with Nasty Noise Positive and Unlabeled Examples Help Learning Learning Real Polynomials with a Turing Machine Faster Near-Optimal Reinforcement Learning: Adding Adaptiveness to the E3 Algorithm, Includes bibliographical references and index