Algorithmic Learning Theory

Table of Contents

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   Book Overview
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   Chapter 1 Extracting Information from the Web for Concept Learning and Collaborative Filtering
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   Chapter 2 The Divide-and-Conquer Manifesto
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   Chapter 3 Sequential Sampling Techniques for Algorithmic Learning Theory
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   Chapter 4 Towards an Algorithmic Statistics
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   Chapter 5 Minimum Message Length Grouping of Ordered Data
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   Chapter 6 Learning From Positive and Unlabeled Examples
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   Chapter 7 Learning Erasing Pattern Languages with Queries
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   Chapter 8 Learning Recursive Concepts with Anomalies
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    Chapter 9 Identification of Function Distinguishable Languages
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    Chapter 10 A Probabilistic Identification Result
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    Chapter 11 A New Framework for Discovering Knowledge from Two-Dimensional Structured Data Using Layout Formal Graph System
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    Chapter 12 Hypotheses Finding via Residue Hypotheses with the Resolution Principle
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    Chapter 13 Conceptual Classifications Guided by a Concept Hierarchy
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    Chapter 14 Learning Taxonomic Relation by Case-based Reasoning
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    Chapter 15 Average-Case Analysis of Classification Algorithms for Boolean Functions and Decision Trees
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    Chapter 16 Self-duality of Bounded Monotone Boolean Functions and Related Problems
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    Chapter 17 Sharper Bounds for the Hardness of Prototype and Feature Selection
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    Chapter 18 On the Hardness of Learning Acyclic Conjunctive Queries
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    Chapter 19 Dynamic Hand Gesture Recognition Based On Randomized Self-Organizing Map Algorithm
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    Chapter 20 On Approximate Learning by Multi-layered Feedforward Circuits
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    Chapter 21 The Last-Step Minimax Algorithm
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    Chapter 22 Rough Sets and Ordinal Classification
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    Chapter 23 A note on the generalization performance of kernel classifiers with margin
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    Chapter 24 On the Noise Model of Support Vector Machines Regression
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    Chapter 25 Computationally Efficient Transductive Machines