Algorithmic learning theory : 10th International Conference, ALT&#039,99, Tokyo, Japan, December 6-8, 1999 : proceedings

Algorithmic Learning Theory: 10th International Conference, ALT’99 Tokyo, Japan, December 6–8, 1999 Proceedings
Author: Osamu Watanabe, Takashi Yokomori
Published by Springer Berlin Heidelberg
ISBN: 978-3-540-66748-3
DOI: 10.1007/3-540-46769-6

Table 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