Machine learning : proceedings of the eleventh international conference /

Machine Learning Proceedings 1994.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: International Conference on Machine Learning
Otros Autores: Cohen, William W. (Editor ), Hirsh, Haym, 1963- (Editor )
Formato: Electrónico Congresos, conferencias eBook
Idioma:Inglés
Publicado: San Francisco, CA : Morgan Kaufmann, [1994]
Temas:
Machine learning > Congresses.
Apprentissage automatique > Congr�es.
COMPUTERS > General.
Machine learning
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; WORKSHOPS; TUTORIALS; ORGANIZING COMMITTEE; PROGRAM COMMITTEE; PART 1: CONTRIBUTED PAPERS; Chapter 1. A New Method for Predicting Protein Secondary Structures Based on StochasticTree Grammars; Abstract; 1 Introduction; 2 Stochastic Ranked NodeRewriting Grammars; 3 Modeling Beta Sheet Structureswith RNRG; 4 Learning and Parsing of aRestricted Subclass; 5 Experimental Results; 6 Concluding Remarks; References; Chapter 2. Learning Recursive Relationswith Randomly Selected Small Training Sets; Abstract; 1 MOTIVATION.
  • 2 Review of CRUSTACEAN3 EVALUATION; 4 RELATED WORK; 5 CONCLUSION; Acknowledgements; References; Chapter 3. Improving Accuracy of Incorrect DomainTheories; Abstract; 1 INTRODUCTION; 2 KNOWLEDGE INTENSIVETHEORY REFINEMENT; 3 A DESCRIPTION OF GENTRE; 4 EXPERIMENTAL EVALUATION; 5 CONCLUSIONS; Acknowledgements; References; Chapter 4. Greedy Attribute Selection; Abstract; 1 INTRODUCTION; 2 ATTRIBUTE SELECTION IN CAP; 3 ATTRIBUTE HILLCLIMBING; 4 CACHING TO SPEED SEARCH; 5 EMPIRICAL ANALYSIS; 6 FOCUS and RELIEF; 7 CONCLUSION; Acknowledgements; References.
  • Chapter 5. Using Sampling and Queries to Extract Rules from Trained Neural NetworksAbstract; 1 INTRODUCTION; 2 RULE EXTRACTION ASSEARCH; 3 RULE EXTRACTION ASLEARNING; 4 EXTRACTING M-of-NRULES; 5 FUTURE WORK; 6 CONCLUSIONS; Acknowledgements; References; Chapter 6. The Generate, Test, and Explain Discovery System Architecture; Abstract; 1 INTRODUCTION AND MOTIVATION; 2 ARCHITECTURE; 3 APPLICATIONS; 4 RELATED WORK; 5 LIMITATIONS, FUTURE WORK, ANDCONCLUSIONS; Acknowledgments; References; Chapter 7. Boosting and Other Machine Learning Algorithms; Abstract; 1. INTRODUCTION; 2. PROCEDURE.
  • 3. OTHER MACHINE LEARNINGTECHNIQUES4. CONCLUSIONS; References; Chapter 8. In Defense of C4.5: Notes on Learning One-Level Decision Trees; Abstract; 1 INTRODUCTION; 2 PREDICTION ACCURACY; 3 TEST DOMAINS; 4 UPPER BOUND ON CLASSIFICATIONACCURACY; 5 THE COMPLEXITY OF A CLASSIFIER; 6 RELATED WORK; 7 CONCLUSION; Acknowledgements; References; Chapter 9. Incremental Reduced Error Pruning; Abstract; 1 INTRODUCTION; 2 SOME PROBLEMS WITH REDUCEDERROR PRUNING; 3 COHEN'S GROWALGORITHM; 4 INCREMENTALREP; 5 EXPERIMENTS; 6 CONCLUSION; Acknowledgements; References.
  • Chapter 10. An Incremental Learning Approach for CompletablePlanningAbstract; 1 INTRODUCTION; 2 COMPLETABLE PLANNING; 3 LEARNING COMPLETABLE PLANS; 4 EXPERIMENTS; 5 DISCUSSION; Acknowledgments; References; Chapter 11. Learning by Experimentation: Incremental Refinement of Incomplete Planning Domains; Abstract; 1 Introduction; 2 Planning with Incomplete Models; 3 Incremental Refinement of PlanningDomains through Experimentation; 4 Empirical Results; 5 Conclusion; Acknowledgments; References; Chapter 12. Learning Disjunctive Concepts by Means of GeneticAlgorithms; Abstract; 1 INTRODUCTION.

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