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Machine Learning : An Artificial Intelligence Approach (Volume I).

Machine Learning : An Artificial Intelligence Approach (Volume I).
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10022973 Access this eBook online Ebook for Computer Science   GUtech Library . . Available .  
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ISBN 9780080510545
9780934613095
Author Michalski, Ryszard S.
Title Machine Learning : : An Artificial Intelligence Approach (Volume I).
Description 1 online resource (585 pages)
Contents Front Cover -- Machine Learning: An Artificial Intelligence Approach -- Copyright Page -- 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 Underlying Proof Generation -- 7.3 Learning.
7.4 Knowledge Compilation -- 7.5 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 Heuristics -- 9.6 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 -- 11.4 A Criterion of Clustering Quality -- 11.5 Method and Implementation -- 11.6 An Example of a Practical Problem: Constructing a Classification Hierarchy of Spanish Folk Songs -- 11.7 Summary and Some Suggested Extensions of the Method -- PART FIVE: LEARNING FROM INSTRUCTION -- Chapter 12. Machine Transformation of Advice into a Heuristic Search Procedure -- 12.1 Introduction -- 12.2 Kinds of Knowledge Used -- 12.3 A Slightly Non-Standard Definition of Heuristic Search -- 12.4 Instantiating the HSM Schema for a Given Problem -- 12.5 Refining HSM by Moving Constraints Between Control Components -- 12.6 Evaluation of Generality.
12.7 Conclusion -- 12.A Index of Rules -- Chapter 13. Learning by Being Told: Acquiring Knowledge for Information Management -- 13.1 Overview -- 13.2 Technical Approach: Experiments with the KLAUS Concept -- 13.3 More Technical Details -- 13.4 Conclusions and Directions for Future Work -- 13.A Training NANOKLAUS About Aircraft Carriers -- Chapter 14. The Instructible Production System: A Retrospective Analysis -- 14.1 The Instructive Production System Project -- 14.2 Essential Functional Components of Instructible Systems -- 14.3 Survey of Approaches -- 14.4 Discussion -- PART SIX: APPLIED LEARNING SYSTEMS -- Chapter 15. Learning Efficient Classification Procedures and their Application to Chess End Games -- 15.1 Introduction -- 15.2 The Inductive Inference Machinery -- 15.3 The Lost N-ply Experiments -- 15.4 Approximate Classification Rules -- 15.5 Some Thoughts on Discovering Attributes -- 15.6 Conclusion -- Chapter 16. Inferring Student Models for Intelligent Computer-Aided Instruction -- 16.1 Introduction -- 16.2 Generating a Complete and Non-redundant Set of Models -- 16.3 Processing Domain Knowledge -- 16.4 Summary -- 16.A An Example of the SELECTIVE Algorithm: LMS-I's Model Generation Algorithm -- Comprehensive Bibliography of Machine Learning -- Glossary of Selected Terms In Machine Learning -- About the Authors -- Author Index -- Subject Index.
Subject Artificial intelligence
Machine learning
Other Author Electronic books.
Other name(s) Carbonell, Jaime G.
Mitchell, Tom M.
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