Proceedings of the Sixth International Workshop on Machine Learning, Cornell University, Ithaca, New York, June 26-27, 1989 / editor, workshop chair, Alberto Maria Segre ; editor, Bruce Spatz ; coordinating editor, John Galbraith ; production manager, Shirley Jowell ; cover designer, Jo Jackson.

Format:

Book

Conference/Event

Author/Creator:

International Workshop on Machine Learning, Corporate Author.

Contributor:

Segre, Alberto Maria, editor.

Spatz, Bruce, editor.

Galbraith, John, editor.

Jowell, Shirley, production manager.

Jackson, Jo, cover designer.

Conference Name:

International Workshop on Machine Learning (6th : 1989 : Cornell University)

International Workshop on Machine Learning

Language:

English

Subjects (All):

Machine learning--Congresses.

Machine learning.

Physical Description:

1 online resource (521 p.)

Place of Publication:

San Mateo, California : Morgan Kaufmann Publishers, Inc., 1989.

Language Note:

English

Summary:

Machine Learning Proceedings 1989

Contents:

Front Cover; Proceedings of the Sixth International Workshop on Machine Learning; Copyright Page; Table of Contents; PREFACE; Part 1: Combining Empirical and Explanation-Based Learning; Chapter1. Unifying Themes in Empirical and Explanation-Based Learning; The Need for Unified Theories of Learning; Learning from One Instance and Many Instances; Learning With and Without Search; Learning With and Without Domain Knowledge; Justified and Unjustified Learning; Accuracy and Efficiency in Machine Learning

CHAPTER2. INDUCTION OVER THE UNEXPLAINED: Integrated Learning of Concepts with Both Explainable and Conventional AspectsABSTRACT; INTRODUCTION; THE IOU APPROACH; AN INITIAL IOU ALGORITHM; IOU VERSUS PURE SBL AND IOE; CONCLUSIONS AND FUTURE RESEARCH; CHAPTER3. CONCEPTUAL CLUSTERING OF EXPLANATIONS; INDUCTION-BASED AND EXPLANATION-BASED LEARNING; OPEN PROBLEMS; CONCEPTUAL CLUSTERING OF EXPLANATIONS; CONCLUDING REMARKS; References; Chapter4. A Tight Integration of Deductive and Inductive Learning; 1 Introduction; 2 A new integration framework: generalized explanations; 3 An application example

ReferencesCHAPTER5. MULTI-STRATEGY LEARNING IN NONHOMOGENEOUS DOMAIN THEORIES; ABSTRACT; INTRODUCTION; DISCIPLE AS AN EXPERT SYSTEM; THE LEARNING PROBLEM; LEARNING IN A COMPLETE THEORY DOMAIN; LEARNING IN A WEAK THEORY DOMAIN; CONCLUSIONS; References; CHAPTER 6. A DESCRIPTION OF PREFERENCE CRITERION IN CONSTRUCTIVE LEARNING: A Discussion of Basic Issues; 1. INTRODUCTION; 2. CONSTRUCTIVE LEARNING; 3. INDIVIDUAL CRITERIA AND THEIR RELATIONSHIPS; Acknowledgements; Reference; CHAPTER 7. COMBINING CASE-BASED REASONING, EXPLANATION-BASED LEARNING, AND LEARNING FROM INSTRUCTION; ABSTRACT

INTRODUCTIONINFERRING IN STRUCTOR'S GOAL; INFERRING PLACE IN CURRENT DIAGNOSIS; ADJUSTING THE SALIENCE OF FEATURES; CAUSAL EXPLANATION OF ACTIONS; CONCLUSION; References; CHAPTER 8. DEDUCTION IN TOP-DOWN INDUCTIVE LEARNING; References; CHAPTER 9. ONE-SIDED ALGORITHMS FOR INTEGRATING EMPIRICAL AND EXPLANATION-BASED LEARNING; A FRAMEWORK FOR INTEGRATED LEARNING; PERFORMANCE AND FOUNDATIONAL EXAMPLES; THE IOSC andk-IOSCNF ALGORITHM; CONCLUSION; References; CHAPTER 10. COMBINING EMPIRICAL AND ANALYTICAL LEARNING WITH VERSION SPACES; ABSTRACT; INTRODUCTION

USING INCREMENTAL VERSION-SPACE MERGING ON THE RESULTS OF EBGPERSPECTIVES; RELATED WORK; SUMMARY; References; CHAPTER 11. FINDING NEW RULES FOR INCOMPLETE THEORIES: EXPLICIT BIASES FOR INDUCTION WITH CONTEXTUAL INFORMATION; INTRODUCTION; HEURISTICS EXPLOITING CONTEXTUAL INFORMATION AS A STRONG INDUCTIVE BIAS; EMPIRICAL SELECTION OF BIASES; CONCLUSION; Acknowledgments; REFERENCES; CHAPTER 12. LEARNING FROM PLAUSIBLE EXPLANATIONS; INTRODUCTION; THE LEARNING METHOD; CONCLUSION; References; CHAPTER 13. AUGMENTING DOMAIN THEORY FOR EXPLANATION-BASED GENERALISATION; INTRODUCTION

AUGMENTING THE DOMAIN THEORY

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

Description based on print version record.

ISBN:

9781483297408

1483297403

OCLC:

1180288184