Proceedings of the Sixth International Workshop on Machine Learning, Cornell University, Ithaca, New York, June 26-27, 1989
Machine Learning Proceedings 1989
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| Autor Corporativo: | |
| Otros Autores: | , , , , |
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
San Mateo, California :
Morgan Kaufmann Publishers, Inc
1989.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009633559806719 |
Tabla de Contenidos:
- 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
