Machine learning : proceedings of the ninth international workshop (ML92) /

Machine Learning Proceedings 1992.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: International Conference on Machine Learning
Otros Autores: Sleeman, D., Edwards, Peter
Formato: Electrónico Congresos, conferencias eBook
Idioma:Inglés
Publicado: San Mateo, Calif. : M. Kaufman, �1992.
Temas:
Machine learning > Congresses.
Apprentissage automatique > Congr�es.
Machine learning
Machine-learning.
Intelligence artificielle > Congr�es.
Congress
proceedings (reports)
Conference papers and proceedings
Conference papers and proceedings.
Actes de congr�es.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Front Cover; Machine Learning; Copyright Page; Table of Contents; Preface; Program Committee; ML 92 Informal Workshop Themes and Coordinators; Chapter 1. Generalizing from Case Studies: A Case Study; Abstract; 1 PROBLEM AND OBJECTIVES; 2 GENERALIZING CASE STUDIES; 3 AN APPLICATION; 4 LIMITATIONS; 5 CONCLUSION; Acknowledgements; References; Chapter 2. On Learning More Concepts; Abstract; 1 INTRODUCTION; 3 UPPER BOUND ON COVERAGE; 4 THE MULTI-BALLS LEARNING ALGORITHM; 5 THE LARGE-BALL LEARNING ALGORITHM; 6 COVERAGE OF CURRENT LEARNING ALGORITHMS; 7 DISCUSSION; Acknowledgements; References
  • Chapter 3. The Principal Axes Method for Constructive InductionAbstract; 1 INTRODUCTION; 2 LEARNING PRINCIPAL AXES; 3 DISTANCE METRIC; 4 GENERATING SIMILARITY MATRIX; 5 DESCRIPTION SPACE TRANSFORMATION; 6 EMPIRICAL EVALUATION; 7 SUMMARY; Acknowledgments; References; Chapter 4. Learning by Incomplete Explanations of Failures in Recursive Domains; Abstract; 1 Introduction; 2 Means-ends analysis search in recursive domains; 3 Problem solving and learning in FS2; 4 Experimental results; 5 Related work; 6 Conclusions and future work; References
  • Chapter 5. Eliminating Redundancy in Explanation-Based LearningAbstract; 1 INTRODUCTION; 2 PRELIMINARIES; 3 EXAMPLE-GUIDEDUNFOLDING; 4 EXPERIMENTAL RESULTS; 5 RELATED WORK; 6 CONCLUDING REMARKS; References; Chapter 6. Trading off Consistency and Efficiency in Version-Space Induction; Abstract; 1 INTRODUCTION; 2 LEARNING WITH VARIABLE-FACTORED CONJUNCTIVE CONCEPT LANGUAGES; 3 THE FCE LEARNING ALGORITHM; 4 UTILITY; 5 RELATION TO INDUCTIVE LANGUAGE SHIFT; 6 CONCLUSION; Acknowledgements; References; Chapter 7. Peepholing: choosing attributes efficiently for megainduction; Abstract
  • 1 INTRODUCTION AND MOTIVATION2 PEEPHOLING; 3 SHORTLISTING; 4 BLINKERING; 5 EMPIRICAL EVALUATION; 6 CONCLUSIONS; Acknowledgements; References; Chapter 8. Improving Path Planning with Learning; Abstract; 1 INTRODUCTION; 2 ALGORITHM; 3 GENERAL ANALYSIS; 4 SPECIFIC CASE ANALYSIS; 5 COMPUTATIONAL EXPERIENCE; 6 FUTURE WORK; 7 CONCLUSION; Acknowledgements; References; CHAPTER 9. THE RIGHT REPRESENTATION FOR DISCOVERY: FINDING THE CONSERVATION OF MOMENTUM; Abstract; 1 INTRODUCTION; 2 CONSERVATION OF MOMENTUM; 3 CONVENTIONAL MATHEMATICAL APPROACH; 4 THE DIAGRAMMATIC APPROACH; 5 DISCUSSION
  • 6 CONCLUSIONSAcknowledgements; References; Chapter 10. Learning to Predict in Uncertain Continuous Tasks; Abstract; 1 Introduction; 2 Assumptions; 3 Manipulation Tasks; 4 Noise and Uncertainty; 5 Generalization; 6 Funnels; 7 Learning Funnels; 8 Experiments; 9 Assumptions Revisited; Acknowledgements; References; Chapter 11. Lazy Partial Evaluation: An Integration of Explanation-Based Generalisation and Partial Evaluation; Abstract; 1 Introduction; 2 Lazy Partial Evaluation; 3 Application to Constraint Satisfaction; 4 Discussion; 5 Conclusion; Acknowledgements and Availability; References

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