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Machine Learning

Overview of attention for book
Cover of 'Machine Learning'

Table of Contents

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    Book Overview
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    Chapter 1 An Overview of Machine Learning
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    Chapter 2 Why Should Machines Learn?
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    Chapter 3 A Comparative Review of Selected Methods for Learning from Examples
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    Chapter 4 A Theory and Methodology of Inductive Learning
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    Chapter 5 Learning by Analogy: Formulating and Generalizing Plans from Past Experience
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    Chapter 6 Learning by Experimentation: Acquiring and Refining Problem-Solving Heuristics
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    Chapter 7 Acquisition of Proof Skills in Geometry
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    Chapter 8 Using Proofs and Refutations to Learn from Experience
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    Chapter 9 The Role of Heuristics in Learning by Discovery: Three Case Studies
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    Chapter 10 Rediscovering Chemistry with the Bacon System
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    Chapter 11 Learning from Observation: Conceptual Clustering
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    Chapter 12 Machine Transformation of Advice Into a Heuristic Search Procedure
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    Chapter 13 Learning By Being Told: Acquiring Knowledge for Information Management
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    Chapter 14 The Instructible Production System: A Retrospective Analysis
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    Chapter 15 Learning Efficient Classification Procedures and Their Application to Chess End Games
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    Chapter 16 Inferring Student Models for Intelligent Computer-Aided Instruction
Attention for Chapter 2: Why Should Machines Learn?
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