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Algorithmic Learning Theory

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Cover of 'Algorithmic Learning Theory'

Table of Contents

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    Book Overview
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    Chapter 1 Tailoring Representations to Different Requirements
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    Chapter 2 Theoretical Views of Boosting and Applications
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    Chapter 3 Extended Stochastic Complexity and Minimax Relative Loss Analysis
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    Chapter 4 Algebraic Analysis for Singular Statistical Estimation
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    Chapter 5 Generalization Error of Linear Neural Networks in Unidentifiable Cases
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    Chapter 6 The Computational Limits to the Cognitive Power of the Neuroidal Tabula Rasa
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    Chapter 7 The Consistency Dimension and Distribution-Dependent Learning from Queries (Extended Abstract)
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    Chapter 8 The VC-Dimension of Subclasses of Pattern Languages
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    Chapter 9 On the V γ Dimension for Regression in Reproducing Kernel Hilbert Spaces
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    Chapter 10 On the Strength of Incremental Learning
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    Chapter 11 Learning from Random Text
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    Chapter 12 Inductive Learning with Corroboration
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    Chapter 13 Flattening and Implication
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    Chapter 14 Induction of Logic Programs Based on ψ -Terms
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    Chapter 15 Complexity in the Case Against Accuracy: When Building One Function-Free Horn Clause Is as Hard as Any
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    Chapter 16 A Method of Similarity-Driven Knowledge Revision for Type Specializations
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    Chapter 17 PAC Learning with Nasty Noise
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    Chapter 18 Positive and Unlabeled Examples Help Learning
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    Chapter 19 Learning Real Polynomials with a Turing Machine
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    Chapter 20 Faster Near-Optimal Reinforcement Learning: Adding Adaptiveness to the E 3 Algorithm
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    Chapter 21 A Note on Support Vector Machine Degeneracy
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    Chapter 22 Learnability of Enumerable Classes of Recursive Functions from “Typical” Examples
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    Chapter 23 On the Uniform Learnability of Approximations to Non-recursive Functions
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    Chapter 24 Learning Minimal Covers of Functional Dependencies with Queries
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    Chapter 25 Boolean Formulas Are Hard to Learn for Most Gate Bases
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    Chapter 26 Finding Relevant Variables in PAC Model with Membership Queries
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    Chapter 27 General Linear Relations among Different Types of Predictive Complexity
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    Chapter 28 Predicting Nearly as Well as the Best Pruning of a Planar Decision Graph
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    Chapter 29 On Learning Unions of Pattern Languages and Tree Patterns
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Title
Algorithmic Learning Theory
Published by
Springer Science & Business Media, November 1999
DOI 10.1007/3-540-46769-6
ISBNs
978-3-54-066748-3, 978-3-54-046769-4
Editors

Watanabe, Osamu, Yokomori, Takashi

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