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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.
| Clasificación: | Libro Electrónico |
|---|---|
| Autores Corporativos: | , |
| Otros Autores: | , |
| Formato: | Electrónico Congresos, conferencias eBook |
| Idioma: | Inglés |
| Publicado: |
Los Altos, CA :
M. Kaufmann Publishers,
�1987.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 111 | 2 | |a International Workshop on Machine Learning |n (4th : |d 1987 : |c Irvine, Calif.) | |
| 245 | 1 | 0 | |a Proceedings of the Fourth International Workshop on Machine Learning : |b June 22-25, 1987, University of California, Irvine / |c editor-program chair, Pat Langley ; administrative organizer, Caroline Ehrlich ; sponsored by American Association for Artificial Intelligence [and others]. |
| 264 | 1 | |a Los Altos, CA : |b M. Kaufmann Publishers, |c �1987. | |
| 300 | |a 1 online resource (vi, 403 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 520 | |a Proceedings of the Fourth International Workshop on MACHINE LEARNING. | ||
| 546 | |a English. | ||
| 650 | 0 | |a Machine learning |v Congresses. | |
| 650 | 6 | |a Apprentissage automatique |0 (CaQQLa)201-0131435 |v Congr�es. |0 (CaQQLa)201-0378219 | |
| 650 | 7 | |a COMPUTERS |x General. |2 bisacsh | |
| 650 | 7 | |a Machine learning |2 fast |0 (OCoLC)fst01004795 | |
| 655 | 2 | |a Congress |0 (DNLM)D016423 | |
| 655 | 7 | |a proceedings (reports) |2 aat |0 (CStmoGRI)aatgf300027316 | |
| 655 | 7 | |a Conference papers and proceedings |2 fast |0 (OCoLC)fst01423772 | |
| 655 | 7 | |a Conference papers and proceedings. |2 lcgft | |
| 655 | 7 | |a Actes de congr�es. |2 rvmgf |0 (CaQQLa)RVMGF-000001049 | |
| 700 | 1 | |a Langley, Pat, |e editor. | |
| 700 | 1 | |a Ehrlich, Caroline, |e contributor. | |
| 710 | 2 | |a American Association for Artificial Intelligence. | |
| 776 | 0 | 8 | |i Print version: |a International Workshop on Machine Learning (4th : 1987 : Irvine, Calif.). |t Proceedings of the Fourth International Workshop on Machine Learning |z 0934613419 |w (DLC) 87003803 |w (OCoLC)15630877 |
| 856 | 4 | 0 | |u https://sciencedirect.uam.elogim.com/science/book/9780934613415 |z Texto completo |