http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Michalski, Ryszard S. (Ryszard Stanisław)
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 c.
Front Cover; Machine Learning: An Artificial Intelligence Approach; Copyright Page; Table of Contents; PREFACE; PART ONE: GENERAL ISSUES; CHAPTER 1. RESEARCH IN MACHINE LEARNING: Recent Progress, Classification of Methods, and Future Directions; Abstract; 1.1 INTRODUCTION; 1.2 RECENT DEVELOPMENTS; 1.3 SYNTHETIC VERSUS ANALYTIC LEARNING; 1.4 A MULTICRITERIA CLASSIFICATION OF LEARNING PROCESSES; 1.5 A BRIEF REVIEW OF THE CHAPTERS; 1.5 FRONTIER PROBLEMS; ACKNOWLEDGMENTS; References; CHAPTER 2. EXPLANATIONS, MACHINE LEARNING, AND CREATIVITY; Abstract; 2.1 INTRODUCTION"WHAT IS INTELLIGENCE?
2.2 EXPLANATION AND THE UNDERSTANDING PROCESS2.3 EXPLANATION GOALS; 2.4 EXPLANATION IN ACTION; 2.5 INDEXING MEMORY: A KEY ISSUE; 2.6 CREATIVITY: AN ALGORITHMIC PROCESS; 2.7 EXPLAINING SWALE'S DEATH; 2.8 CREATIVE PROBLEM SOLVING; 2.9 CONCLUSION; References; COMMENTARY; References; PART TWO: EMPIRICAL LEARNING METHODS; CHAPTER 3. LEARNING FLEXIBLE CONCEPTS: Fundamental Ideas and a Method Based on Two-Tiered Representation; Abstract; 3.1 INTRODUCTION; 3.2 TWO-TIERED CONCEPT REPRESENTATION; 3.3 EXAMPLES ILLUSTRATING TWO-TIERED REPRESENTATION; 3.4 TRADING BCR FOR ICI.
3.5 LEARNING TWO-TIERED REPRESENTATIONS3.6 RELATING INSTANCES TO CONCEPTS: FLEXIBLE MATCHING; 3.7 EXPERIMENTS WITH AQTT-15; 3.8 A COMPARISON WITH THE ASSISTANT PROGRAM; 3.9 CONCLUSION AND TOPICS FOR FUTURE RESEARCH; ACKNOWLEDGMENTS; References; COMMENTARY; Abstract; 1 OVERVIEW OF THE PAPER; 1.1 Sample Learning Program: AQ15; 2 ANOTHER VIEW OF MULTITIER LEARNING; References; CHAPTER 4. PROTOS: AN EXEMPLAR-BASED LEARNING APPRENTICE; Abstract; 4.1 INTRODUCTION; 4.2 ISSUES IN EXEMPLAR-BASED SYSTEMS AND THEIR SOLUTIONS IN PROTOS; 4.3 AN EXAMPLE OF CLASSIFYING AND LEARNING.
4.4 EXPERIMENTAL EVALUATION OF PROTOS4.5 SUMMARY; ACKNOWLEDGMENTS; References; COMMENTARY; 1 KNOWLEDGE COMPILATION; 2 KNOWLEDGE-BASED PATTERN MATCHING; 3 THE LANGUAGE FOR EXPRESSING JUSTIFICATION; References; CHAPTER 5. PROBABILISTIC DECISION TREES; Abstract; 5.1 INTRODUCTION; 5.2 GROWING DECISION TREES; 5.3 IMPERFECT LEAVES; 5.4 UNKNOWN AND IMPRECISE ATTRIBUTE VALUES; 5.5 SOFT THRESHOLDS; 5.6 CONCLUSION; ACKNOWLEDGMENTS; References; CHAPTER 6. INTEGRATING QUANTITATIVE AND QUALITATIVE DISCOVERY IN THE ABACUS SYSTEM; Abstract; 6.1 INTRODUCTION; 6.2 GOALS FOR QUANTITATIVE DISCOVERY.
6.3 RELATED WORK6.4 THE ABACUS APPROACH TO QUANTITATIVE DISCOVERY; 6.5 DISCOVERING EQUATIONS; 6.6 FORMULATION OF QUALITATIVE PRECONDITIONS; 6.7 EXPERIMENTS; 6.8 DISCUSSION OF METHODOLOGY; 6.9 SUMMARY; ACKNOWLEDGMENTS; References; CHAPTER 7. LEARNING BY EXPERIMENTATION: THE OPERATOR REFINEMENT METHOD; Abstract; 7.1 INTRODUCTION: THE NEED FOR REACTIVE EXPERIMENTATION; 7.2 THE ROLE OF EXPERIMENTATION IN PRODIGY; 7.3 RELATED WORK; 7.4 DISCUSSION AND FURTHER WORK; ACKNOWLEDGMENTS; APPENDIX I THE PRODIGY ARCHITECTURE; References.
contributors, E. Ray Bareiss [and 38 others] ; editors, Yves Kodratoff, Ryszard S. Michalski.
Includes bibliographical references and indexes.