Machine learning : an artificial intelligence approach
著者
書誌事項
Machine learning : an artificial intelligence approach
Morgan Kaufmann, c1983-c1994
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注記
[v. 1]: Reprint. Originally published: Palo Alto, Calif. : Tioga Pub. Co., c1983
v. 3: edited by Yves Kodratoff and Ryszard S. Michalski
v. 4: Machine learning : a multistrategy approach / Ryszard S. Michalski, Gheorghe Tecuci, eds
Includes bibliographies and indexes
内容説明・目次
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[v. 1] ISBN 9780934613095
内容説明
目次
- 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 Instructive Production System: A Retrospective Analysis 14.1 The Instructive Production System Project 14.2 Essential Functional Components of Instructive 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
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v. 3 ISBN 9781558601192
内容説明
目次
- Preface Part One General Issues Chapter 1 Research in Machine Learning
- Recent Progress, Classification of Methods, and Future Directions Chapter 2 Explanations, Machine Learning, and Creativity Part Two Empirical Learning Methods Chapter 3 Learning Flexible Concepts: Fundamental Ideas and a Method Bases on Two-Tiered Representation Chapter 4 Protos: An Exemplar-Based Learning Apprentice Chapter 5 Probabilistic Decision Trees Chapter 6 Integrating Quantitative and Qualitative Discovery in the ABACUS System Chapter 7 Learning by Experimentation: The Operator Refinement Method Chapter 8 Learning Fault Diagnosis Heuristics from Device Descriptions Chapter 9 Conceptual Clustering and Categorization: Bridging the Gap between Induction and Causal Models Part Three Analytical Learning Methods Chapter 10 LEAP: A Learning Apprentice System for VLSI Design Chapter 11 Acquiring General Iterative Concepts by Reformulating Explanations of Observed Examples Chapter 12 Discovering Algorithms from Weak Methods Chapter 13 OGUST: A System That Learns Using Domain Properties Expressed as Theorems Chapter 14 Conditional Operationality and Explanation-based Generalization Part Four Integrated Learning Systems Chapter 15 The Utility of Similarity-based Learning in a World Needing Explanation Chapter 16 Learning Expert Knowledge by Improving the Explanations Provided by the System Chapter 17 Guiding Induction with Domain Theories Chapter 18 Knowledge Base Refinement as Improving an Incorrect and Incomplete Domain Theory Chapter 19 Apprenticeship Learning in Imperfect Domain Theories Part Five Subsymbolic and Heterogenous Learning Systems Chapter 20 Connectionist Learning Procedures Chapter 21 Genetic-Algorithm-based Learning Part Six Formal Analysis Chapter 22 Applying Valiant's Learning Framework to AI Concept-Learning Problems Chapter 23 A New Approach to Unsupervised Learning in Deterministic Environments Bibliography of Recent Machine Learning Research (1985-1989) About the Authors Author Index Subject Index
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v. 4 ISBN 9781558602519
内容説明
目次
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