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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 Editors’ Introduction
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    Chapter 2 Invention and Artificial Intelligence
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    Chapter 3 The Arrowsmith Project: 2005 Status Report
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    Chapter 4 Algorithmic Learning Theory
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    Chapter 5 Algorithms and Software for Collaborative Discovery from Autonomous, Semantically Heterogeneous, Distributed Information Sources
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    Chapter 6 Training Support Vector Machines via SMO-Type Decomposition Methods
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    Chapter 7 Measuring Statistical Dependence with Hilbert-Schmidt Norms
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    Chapter 8 An Analysis of the Anti-learning Phenomenon for the Class Symmetric Polyhedron
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    Chapter 9 Learning Causal Structures Based on Markov Equivalence Class
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    Chapter 10 Stochastic Complexity for Mixture of Exponential Families in Variational Bayes
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    Chapter 11 ACME: An Associative Classifier Based on Maximum Entropy Principle
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    Chapter 12 Constructing Multiclass Learners from Binary Learners: A Simple Black-Box Analysis of the Generalization Errors
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    Chapter 13 On Computability of Pattern Recognition Problems
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    Chapter 14 Algorithmic Learning Theory
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    Chapter 15 Learnability of Probabilistic Automata via Oracles
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    Chapter 16 Learning Attribute-Efficiently with Corrupt Oracles
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    Chapter 17 Learning DNF by Statistical and Proper Distance Queries Under the Uniform Distribution
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    Chapter 18 Learning of Elementary Formal Systems with Two Clauses Using Queries
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    Chapter 19 Gold-Style and Query Learning Under Various Constraints on the Target Class
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    Chapter 20 Non U-Shaped Vacillatory and Team Learning
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    Chapter 21 Learning Multiple Languages in Groups
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    Chapter 22 Inferring Unions of the Pattern Languages by the Most Fitting Covers
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    Chapter 23 Identification in the Limit of Substitutable Context-Free Languages
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    Chapter 24 Algorithms for Learning Regular Expressions
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    Chapter 25 A Class of Prolog Programs with Non-linear Outputs Inferable from Positive Data
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    Chapter 26 Algorithmic Learning Theory
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    Chapter 27 Online Allocation with Risk Information
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    Chapter 28 Defensive Universal Learning with Experts
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    Chapter 29 On Following the Perturbed Leader in the Bandit Setting
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    Chapter 30 Mixture of Vector Experts
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    Chapter 31 On-line Learning with Delayed Label Feedback
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    Chapter 32 Monotone Conditional Complexity Bounds on Future Prediction Errors
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    Chapter 33 Non-asymptotic Calibration and Resolution
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    Chapter 34 Defensive Prediction with Expert Advice
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    Chapter 35 Defensive Forecasting for Linear Protocols
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    Chapter 36 Teaching Learners with Restricted Mind Changes
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Title
Algorithmic Learning Theory
Published by
Springer, Berlin, Heidelberg, January 2005
DOI 10.1007/11564089
ISBNs
978-3-54-029242-5, 978-3-54-031696-1
Editors

Sanjay Jain, Hans Ulrich Simon, Etsuji Tomita

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