Machine learning : an artificial intelligence approach

著者

• Amarel, Saul
• Michalski, Ryszard Stanisław
• Carbonell, Jaime Guillermo
• Mitchell, Tom Michael
• Kodratoff, Yves
• Tecuci, Gheorghe

書誌事項

Machine learning : an artificial intelligence approach

contributors, Saul Amarel ... [et al.] ; editors, Ryszard S. Michalski, Jaime G. Carbonell, Tom M. Mitchell

Morgan Kaufmann, c1983-c1994

• [v. 1]
• v. 2
• v. 3
• v. 4

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

巻冊次

[v. 1] ISBN 9780934613095

内容説明

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.

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

巻冊次

v. 3 ISBN 9781558601192

内容説明

Machine Learning: An Artificial Intelligence Approach, Volume III presents a sample of machine learning research representative of the period between 1986 and 1989. The book is organized into six parts. Part One introduces some general issues in the field of machine learning. Part Two presents some new developments in the area of empirical learning methods, such as flexible learning concepts, the Protos learning apprentice system, and the WITT system, which implements a form of conceptual clustering. Part Three gives an account of various analytical learning methods and how analytic learning can be applied to various specific problems. Part Four describes efforts to integrate different learning strategies. These include the UNIMEM system, which empirically discovers similarities among examples; and the DISCIPLE multistrategy system, which is capable of learning with imperfect background knowledge. Part Five provides an overview of research in the area of subsymbolic learning methods. Part Six presents two types of formal approaches to machine learning. The first is an improvement over Mitchell's version space method; the second technique deals with the learning problem faced by a robot in an unfamiliar, deterministic, finite-state environment.

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

巻冊次

v. 4 ISBN 9781558602519

内容説明

Multistrategy learning is one of the newest and most promising research directions in the development of machine learning systems. The objectives of research in this area are to study trade-offs between different learning strategies and to develop learning systems that employ multiple types of inference or computational paradigms in a learning process. Multistrategy systems offer significant advantages over monostrategy systems. They are more flexible in the type of input they can learn from and the type of knowledge they can acquire. As a consequence, multistrategy systems have the potential to be applicable to a wide range of practical problems. This volume is the first book in this fast growing field. It contains a selection of contributions by leading researchers specializing in this area.

目次

Part One General Issues Part Two Theory Revision Part Three Cooperative Integration Part Four Symbolic and Subsymbolic Learning Part Five Special Topics and Applications

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詳細情報

• NII書誌ID(NCID): BA00240369
• ISBN:
  • 0934613095
  • 0934613001
  • 1558601198
  • 1558602518
• LCCN: 86002953
• LCCN: 82010654
• 出版国コード: us
• タイトル言語コード: eng
• 本文言語コード: eng
• 出版地: San Mateo, Calif.
• ページ数/冊数: 4 v.
• 大きさ: 24 cm
• 分類:
  • LCC : Q325
  • DC19 : 001.53/5
• 件名:
  • LCSH : Machine learning
  • LCSH : Artificial intelligence