rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Machine learning
    sub title: an artificial intelligence approach
    part number: Volume III
  name:
    name part: Bareiss, E. Ray
    role:
      role term: author.
  name:
    name part: Kodratoff, Yves
    role:
      role term: editor.
  name:
    name part: Michalski, Ryszard S. (Ryszard Stanisław)
    name part: 1937-2007
    role:
      role term: editor.
  name:
    name part: ScienceDirect (Online service)
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: cau
    date issued: 1990
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource : illustrations
  abstract: 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.
  table of contents: 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?
  table of contents: 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.
  table of contents: 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.
  table of contents: 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.
  table of contents: 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.
  target audience: specialized
  note: contributors, E. Ray Bareiss [and 38 others] ; editors, Yves Kodratoff, Ryszard S. Michalski.
  note: Includes bibliographical references and indexes.
  note: English.
  subject:
    topic: Machine learning
  subject:
    topic: Artificial intelligence
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Machine Learning
  subject:
    topic: Apprentissage automatique
  subject:
    topic: Intelligence artificielle
  subject:
    topic: artificial intelligence
  subject:
    topic: COMPUTERS
    topic: General
  subject:
    topic: Artificial intelligence
  subject:
    topic: Machine learning
  classification: Q325 .M32 1990
  classification: Q335
  classification: 006.3
  related item:
    title info:
      title: Machine learning
    name:
      name part: Bareiss, E. Ray.
    identifier: (OCoLC)862242857
  identifier: 9780080510552
  identifier: 0080510558
  identifier: 1322465533
  identifier: 9781322465531
  identifier: 9781558601192
  identifier: 1558601198
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: OPELS
    record creation date: 141021
    record change date: 20260326211120.9
    record identifier: in00005756844
    language of cataloging:
      language term: eng