Ebook: Algorithmic Learning Theory: 10th International Conference, ALT’99 Tokyo, Japan, December 6–8, 1999 Proceedings

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.

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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.
Content:
Front Matter....Pages I-XI
Tailoring Representations to Different Requirements....Pages 1-12
Theoretical Views of Boosting and Applications....Pages 13-25
Extended Stochastic Complexity and Minimax Relative Loss Analysis....Pages 26-38
Algebraic Analysis for Singular Statistical Estimation....Pages 39-50
Generalization Error of Linear Neural Networks in Unidentifiable Cases....Pages 51-62
The Computational Limits to the Cognitive Power of the Neuroidal Tabula Rasa....Pages 63-76
The Consistency Dimension and Distribution-Dependent Learning from Queries (Extended Abstract)....Pages 77-92
The VC-Dimension of Subclasses of Pattern Languages....Pages 93-105
On the Strength of Incremental Learning....Pages 106-117
Learning from Random Text....Pages 118-131
Inductive Learning with Corroboration....Pages 132-144
Flattening and Implication....Pages 145-156
Induction of Logic Programs Based on ?-Terms....Pages 157-168
Complexity in the Case Against Accuracy: When Building One Function-Free Horn Clause Is as Hard as Any....Pages 169-181
A Method of Similarity-Driven Knowledge Revision for Type Specializations....Pages 182-193
PAC Learning with Nasty Noise....Pages 194-205
Positive and Unlabeled Examples Help Learning....Pages 206-218
Learning Real Polynomials with a Turing Machine....Pages 219-230
Faster Near-Optimal Reinforcement Learning: Adding Adaptiveness to the E3 Algorithm....Pages 231-240
A Note on Support Vector Machine Degeneracy....Pages 241-251
Learnability of Enumerable Classes of Recursive Functions from “Typical” Examples....Pages 252-263
On the Uniform Learnability of Approximations to Non-recursive Functions....Pages 264-275
Learning Minimal Covers of Functional Dependencies with Queries....Pages 276-290
Boolean Formulas Are Hard to Learn for Most Gate Bases....Pages 291-300
Finding Relevant Variables in PAC Model with Membership Queries....Pages 301-312
General Linear Relations among Different Types of Predictive Complexity....Pages 313-322
Predicting Nearly as Well as the Best Pruning of a Planar Decision Graph....Pages 323-334
On Learning Unions of Pattern Languages and Tree Patterns....Pages 335-346
Back Matter....Pages 347-363
....Pages 365-365