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Machine learning : proceedings of the eleventh international conference /
Machine Learning Proceedings 1994.
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | , |
| Formato: | Electrónico Congresos, conferencias eBook |
| Idioma: | Inglés |
| Publicado: |
San Francisco, CA :
Morgan Kaufmann,
[1994]
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 008 | 141124s1994 caua ob 101 0 eng d | ||
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| 020 | |a 9781483298184 |q (electronic bk.) | ||
| 020 | |a 1483298183 |q (electronic bk.) | ||
| 020 | |z 9781558603356 | ||
| 020 | |z 1558603352 |q (pbk.) | ||
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| 072 | 7 | |a COM |x 000000 |2 bisacsh | |
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| 245 | 0 | 0 | |a Machine learning : |b proceedings of the eleventh international conference / |c edited by William W. Cohen, Haym Hirsh. |
| 264 | 1 | |a San Francisco, CA : |b Morgan Kaufmann, |c [1994] | |
| 264 | 4 | |c �1994 | |
| 300 | |a 1 online resource (x, 381 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 | ||
| 500 | |a "Rutgers University, New Brunswick, N.J., July 10-13, 1994." | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 520 | |a Machine Learning Proceedings 1994. | ||
| 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 Cohen, William W., |e editor. | |
| 700 | 1 | |a Hirsh, Haym, |d 1963- |e editor. | |
| 711 | 2 | |a International Conference on Machine Learning |n (11th : |d 1994 : |c New Brunswick, N.J.) | |
| 776 | 0 | 8 | |i Print version: |t Machine learning |z 1558603352 |w (DLC) 94021011 |w (OCoLC)474133639 |
| 856 | 4 | 0 | |u https://sciencedirect.uam.elogim.com/science/book/9781558603356 |z Texto completo |