Proceedings of the Sixth International Workshop on Machine Learning, Cornell University, Ithaca, New York, June 26-27, 1989 /

Machine Learning Proceedings 1989.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: International Workshop on Machine Learning Cornell University
Otros Autores: Segre, Alberto Maria
Formato: Electrónico Congresos, conferencias eBook
Idioma:Inglés
Publicado: San Mateo, Calif. : M. Kaufmann Publishers, �1989.
Temas:
Machine learning > Congresses.
Apprentissage automatique > Congr�es.
Machine learning.
Lernender Automat
Lernender Automat.
Congress
Conference papers and proceedings.
Actes de congr�es.
Kongress.
Acceso en línea:Texto completo
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
  • 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

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