Screen LibraryMachine learning : an artificial intelligence approach /
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...
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| Corporate Authors: | |
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| Group Author: | ; ; ; |
| Published: |
M. Kaufmann,
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| Publisher Address: | Los Altos, Calif. : |
| Publication Dates: | 1983-<c1990> |
| Literature type: | eBook |
| Language: | English |
| Subjects: | |
| Online Access: |
http://www.sciencedirect.com/science/book/9780080510545 |
| Summary: |
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). |
| Item Description: |
Vol. [1] previously published: Palo Alto, Calif. : Tioga Pub. Co., 1983. Vol. 3 edited by Yves Kodratoff and Ryszard S. Michalski. |
| Carrier Form: | 1 online resource (volumes <1-3>) : illustrations |
| Bibliography: | Includes bibliographical references and indexes. |
| ISBN: |
9780080510545 008051054X |
| Index Number: | Q325 |
| CLC: | TP181 |
| Contents: |
Front Cover; Machine Learning: An Artificial Intelligence Approach; Copyright Page; PREFACE; Table of Contents; 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 RemarksPART 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 Experience5.1 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 Underlying Proof Generation; 7.3 Learning. 7.4 Knowledge Compilation7.5 Summary of Geometry Learning; Chapter 8. Using Proofs and Refutations to Learn from Experience; 8.1 Introduction; 8.2 The Learning Cycle; 8.3 Five Heuristics for Rectifying Refuted Theories; 8.4 Computational Problems and Implementation Techniques; 8.5 Conclusions; PART FOUR: LEARNING FROM OBSERVATION AND DISCOVERY; Chapter 9. The Role of Heuristics in Learning by Discovery: Three Case Studies; 9.1 Motivation; 9.2 Overview; 9.3 Case Study 1: The AM Program; Heuristics Used to Develop New Knowledge; 9.4 A Theory of Heuristics; 9.5 Case Study 2: The Eurisko Program. Heuristics Used to Develop New Heuristics9.6 Heuristics Used to Develop New Representations; 9.7 Case Study 3: Biological Evolution; Heuristics Used to Generate Plausible Mutations; 9.8 Conclusions; Chapter 10. Rediscovering Chemistry With the BACON System; 10.1 Introduction; 10.2 An Overview of BACON. 4; 10.3 The Discoveries of BACON. 4; 10.4 Rediscovering Nineteenth Century Chemistry; 10.5 Conclusions; Chapter 11. Learning From Observation: Conceptual Clustering; 11.1 Introduction; 11.2 Conceptual Cohesiveness; 11.3 Terminology and Basic Operations of the Algorithm. |