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Machine learning : proceedings of the ninth international workshop (ML92) /
Machine Learning Proceedings 1992.
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | , |
| Formato: | Electrónico Congresos, conferencias eBook |
| Idioma: | Inglés |
| Publicado: |
San Mateo, Calif. :
M. Kaufman,
�1992.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 0 | 0 | |a Machine learning : |b proceedings of the ninth international workshop (ML92) / |c edited by Derek Sleeman and Peter Edwards. |
| 260 | |a San Mateo, Calif. : |b M. Kaufman, |c �1992. | ||
| 300 | |a 1 online resource (viii, 488 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 indexes. | ||
| 506 | |3 Use copy |f Restrictions unspecified |2 star |5 MiAaHDL | ||
| 533 | |a Electronic reproduction. |b [Place of publication not identified] : |c HathiTrust Digital Library, |d 2011. |5 MiAaHDL | ||
| 538 | |a Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002. |u http://purl.oclc.org/DLF/benchrepro0212 |5 MiAaHDL | ||
| 583 | 1 | |a digitized |c 2011 |h HathiTrust Digital Library |l committed to preserve |2 pda |5 MiAaHDL | |
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 520 | |a Machine Learning Proceedings 1992. | ||
| 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 Machine learning |2 fast |0 (OCoLC)fst01004795 | |
| 650 | 1 | 7 | |a Machine-learning. |2 gtt |
| 650 | 7 | |a Intelligence artificielle |x Congr�es. |2 ram | |
| 650 | 7 | |a Apprentissage automatique |x Congr�es. |2 ram | |
| 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 Sleeman, D. | |
| 700 | 1 | |a Edwards, Peter. | |
| 711 | 2 | |a International Conference on Machine Learning |n (9th : |d 1992 : |c Aberdeen, Scotland) | |
| 776 | 0 | 8 | |i Print version: |t Machine learning. |d San Mateo, Calif. : M. Kaufman, �1992 |w (DLC) 92017244 |w (OCoLC)25914741 |
| 856 | 4 | 0 | |u https://sciencedirect.uam.elogim.com/science/book/9781558602472 |z Texto completo |