Algorithmic Learning Theory: 10th International Conference, ALT '99 Tokyo, Japan, December 6-8, 1999 Proceedings

by Osamu Watanabe (Editor), Takashi Yokomori (Editor)

Overview

This book constitutes the refereed proceedings of the 10th International Conference on Algorithmic Learning Theory, ALT'99, held in Tokyo, Japan, in December 1999.
The 26 full papers presented were carefully reviewed and selected from a total of 51 submissions. Also included are three invited papers. The papers are organized in sections on Learning Dimension, Inductive Inference, Inductive Logic Programming, PAC Learning, Mathematical Tools for Learning, Learning Recursive Functions, Query Learning and On-Line Learning.

Product Details

ISBN-13:	9783540667483
Publisher:	Springer Berlin Heidelberg
Publication date:	01/07/2000
Series:	Lecture Notes in Computer Science , #1720
Edition description:	1999
Pages:	372
Product dimensions:	6.10(w) x 9.25(h) x 0.36(d)

Table of Contents

Invited Lectures.- Tailoring Representations to Different Requirements.- Theoretical Views of Boosting and Applications.- Extended Shastic Complexity and Minimax Relative Loss Analysis.- Regular Contributions.- 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— Dimension for Regression in Reproducing Kernel Hilbert Spaces.- On the Strength of Incremental Learning.- Learning from Random Text.- Inductive Learning with Corroboration.- Flattening and Implication.- Induction of Logic Programs Based on—-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.- A Note on Support Vector Machine Degeneracy.- Learnability of Enumerable Classes of Recursive Functions from “Typical” Examples.- On the Uniform Learnability of Approximations to Non-recursive Functions.- Learning Minimal Covers of Functional Dependencies with Queries.- Boolean Formulas Are Hard to Learn for Most Gate Bases.- Finding Relevant Variables in PAC Model with Membership Queries.- General Linear Relations among Different Types of Predictive Complexity.- Predicting Nearly as Well as the Best Pruning of a Planar Decision Graph.- On Learning Unionsof Pattern Languages and Tree Patterns.