Proceedings of the Fourth International Workshop on Machine Learning : June 22-25, 1987, University of California, Irvine /

Proceedings of the Fourth International Workshop on MACHINE LEARNING.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores Corporativos: International Workshop on Machine Learning Irvine, Calif., American Association for Artificial Intelligence
Otros Autores: Langley, Pat (Editor ), Ehrlich, Caroline (Contribuidor)
Formato: Electrónico Congresos, conferencias eBook
Idioma:Inglés
Publicado: Los Altos, CA : M. Kaufmann Publishers, �1987.
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 ; Proceedings of the Fourth International Workshop on Machine Learning; Copyright Page; Table of Contents ; PREFACE; Chapter 1. Learning about speech sounds:The NEXUS Project; Abstract; 1. Introduction; 2. Instance-Based Learning Mechanisms; 3. Learning in NEXUS; 4. Empirical Tests of NEXUS; 5. Summary; Acknowledgements; References; Chapter 2. Protos: An Exemplar-Based LearningApprentice; Abstract; 1. Introduction; 2. Issues in Exemplar-based Systems and Their Solutions in Protos; 3. An Example of Classifying and Learning; 4. Summary; Acknowledgements; References.
  • Chapter 3. Learning Representative Exemplars of Concepts:An Initial Case StudyAbstract; 1. Introduction; 2. Exemplar Models; 3. Experiment; 4. Experimental Results; 5. Discussion; Acknowledgements; References; CHAPTER4. DECISION TREES AS PROBABILISTIC CLASSIFIERS; Abstract; 1. Introduction; 2. Imperfect Leaves; 3. Unknown and Imprecise Attribute Values; 4. Soft Threshholds; 5� Conclusion; Acknowledgements; References; Chapter 5. Conceptual Clustering, Learning from Examples, and Inference; Abstract; 1� Introduction; 2. An Overview of COBWEB; 3. Classification and Inference.
  • 4. A Note on Inference and Understandability5. Concluding Remarks; Acknowledgements; References; Chapter 6. How toLearn Imprecise Concepts: A Method for Employing a Two-Tiered Knowledge Representation in Learning; Abstract; 1. Introduction; 2. Two-tiered Concept Representation; 3. Using and Learning Concepts with Two-tiered Representation; 4. An Experiment on Learning Decision Rules in Medical Domains; 4. Contusion; Ackowledgements; References; Chapter 7. Quasi-Darwinian Learning in a Classifier System; Abstract; 1. Introduction; 2. A Simple Classifier System; 3. A Learning Example.
  • 4. ConclusionReferences; CHAPTER 8. MORE ROBUST CONCEPT LEARNINGUSING DYNAMICALLY
  • VARIABLE BIAS; Abstract; 1. Introduction; 2. Bias Flexibility and Binding Times; 3. A Closer Look at the VBMS; 4. Implementation, Experiment, and Outlook; 5. Summary and Remarks; Acknowledgements; References; Chapter 9. Incremental Adjustment of Representations for Learning; Abstract; 1. Introduction; 2. Related Work; 3. The STAGGER System; 4. Interactions and Bias; 5. Discussion; Acknowledgements; References; Chapter 10. Concept Learning in Context; Abstract; 1. Introduction and Motivation.
  • 2. MetaLEX's Learning Task3. MetaLEX's Learning Method; 4. Experimental Results; 5. Discussion; 6. Summary; Acknowledgments; References; Chapter 11. Strategy Learning with MultilayerConnectionist Representations; Abstract; 1. Introduction; 2. The Pole-Balancing Task; 3� Specification of the Connectionist Learning System; 4. Results; 5. Discussion; 6. Conclusion; Acknowledgements; References; Chapter 12. Learning a Preference Predicate; Abstract; 1. Introduction; 2� Hypothesis; 3. Best-First Search Revisited; 4. Discussion; References.

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