Machine Learning

1st Edition

An Artificial Intelligence Approach (Volume I)


  • Jaime Carbonell
  • Ryszard Michalski
  • Tom Mitchell
  • Machine Learning

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    Machine Learning: An Artificial Intelligence Approach contains tutorial overviews and research papers representative of trends in the area of machine learning as viewed from an artificial intelligence perspective. The book is organized into six parts. Part I provides an overview of machine learning and explains why machines should learn. Part II covers important issues affecting the design of learning programs—particularly programs that learn from examples. It also describes inductive learning systems. Part III deals with learning by analogy, by experimentation, and from experience. Parts IV and V discuss learning from observation and discovery, and learning from instruction, respectively. Part VI presents two studies on applied learning systems—one on the recovery of valuable information via inductive inference; the other on inducing models of simple algebraic skills from observed student performance in the context of the Leeds Modeling System (LMS). This book is intended for researchers in artificial intelligence, computer science, and cognitive psychology; students in artificial intelligence and related disciplines; and a diverse range of readers, including computer scientists, robotics experts, knowledge engineers, educators, philosophers, data analysts, psychologists, and electronic engineers.

    Table of Contents

    Preface Part One General Issues in Machine Learning Chapter 1 An Overview of Machine Learning 1.1 Introduction 1.2 The Objectives of Machine Learning 1.3 A Taxonomy of Machine Learning Research 1.4 An Historical Sketch of Machine Learning 1.5 A Brief Reader's Guide Chapter 2 Why Should Machines Learn? 2.1 Introduction 2.2 Human Learning and Machine Learning 2.3 What is Learning? 2.4 Some Learning Programs 2.5 Growth of Knowledge in Large Systems 2.6 A Role for Learning 2.7 Concluding Remarks Part Two Learning from Examples Chapter 3 A Comparative Review of Selected Methods for Learning from Examples 3.1 Introduction 3.2 Comparative Review of Selected Methods 3.3 Conclusion Chapter 4 A Theory and Methodology of Inductive Learning 4.1 Introduction 4.2 Types of Inductive Learning 4.3 Description Language 4.4 Problem Background Knowledge 4.5 Generalization Rules 4.6 The Star Methodology 4.7 An Example 4.8 Conclusion 4.A Annotated Predicate Calculus (APC) Part Three Learning in Problem-Solving and Planning Chapter 5 Learning by Analogy: Formulating and Generalizing Plans from Past Experience 5.1 Introduction 5.2 Problem-Solving by Analogy 5.3 Evaluating the Analogical Reasoning Process 5.4 Learning Generalized Plans 5.5 Concluding Remark Chapter 6 Learning by Experimentation: Acquiring and Refining Problem-Solving Heuristics 6.1 Introduction 6.2 The Problem 6.3 Design of LEX 6.4 New Directions: Adding Knowledge to Augment Learning 6.5 Summary Chapter 7 Acquisition of Proof Skills in Geometry 7.1 Introduction 7.2 A Model of the Skill Under


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    © 1983
    Morgan Kaufmann
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