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Algorithmic Learning Theory

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Cover of 'Algorithmic Learning Theory'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    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
Attention for Chapter 4: Algebraic Analysis for Singular Statistical Estimation
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Chapter title
Algebraic Analysis for Singular Statistical Estimation
Chapter number 4
Book title
Algorithmic Learning Theory
Published by
Springer, Berlin, Heidelberg, December 1999
DOI 10.1007/3-540-46769-6_4
Book ISBNs
978-3-54-066748-3, 978-3-54-046769-4
Authors

Sumio Watanabe, Watanabe, Sumio

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 8%
China 1 4%
Czechia 1 4%
Italy 1 4%
Unknown 19 79%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 42%
Professor > Associate Professor 3 13%
Other 2 8%
Student > Master 2 8%
Researcher 2 8%
Other 5 21%
Readers by discipline Count As %
Computer Science 13 54%
Mathematics 3 13%
Engineering 3 13%
Agricultural and Biological Sciences 1 4%
Physics and Astronomy 1 4%
Other 1 4%
Unknown 2 8%