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Machine learning : proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, California, July 9-12, 1995 /
Machine Learning Proceedings 1995.
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | , |
| Formato: | Electrónico Congresos, conferencias eBook |
| Idioma: | Inglés |
| Publicado: |
San Francisco, CA :
Morgan Kaufmann Publishers,
�1995.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 111 | 2 | |a International Conference on Machine Learning |n (12th : |d 1995 : |c Tahoe City, Calif.) | |
| 245 | 1 | 0 | |a Machine learning : |b proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, California, July 9-12, 1995 / |c edited by Armand Prieditis, Stuart Russell. |
| 260 | |a San Francisco, CA : |b Morgan Kaufmann Publishers, |c �1995. | ||
| 300 | |a 1 online resource (xiv, 591 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. | ||
| 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 2010. |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 2010 |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; Advisory Committee; Program Committee; Auxiliary Reviewers; Workshops; Tutorials; PART 1: CONTRIBUTED PAPERS; Chapter 1. On-line Learning of Binary Lexical Relations Using Two-dimensional Weighted Majority Algorithms; ABSTRACT; 1 Introduction; 2 On-line Learning Model for Binary Relations; 3 Two-dimensional Weighted Majority Prediction Algorithms; 4 Experimental Results; 5 Theoretical Performance Analysis; 6 Concluding Remarks; Acknowledgement; References | |
| 505 | 8 | |a Chapter 2. On Handling Tree-Structured Attributes in Decision Tree LearningAbstract; 1 Introduction; 2 Decision Trees With Tree-Structured Attributes; 3 Pre-processing Approaches; 4 A Direct Approach; 5 Analytical Comparison; 6 Experimental Comparison; 7 Summary and Conclusion; Acknowledgement; References; Chapter 3. Theory and Applications of Agnostic PAC-Learning with Small Decision Trees; Abstract; 1 INTRODUCTION; 2 THE AGNOSTIC PAC-LEARNING ALGORITHM T2; 3 EVALUATION OF T2 ON ""REAL-WORLD"" CLASSIFICATION PROBLEMS; 4 LEARNING CURVES FOR DECISION TREES OF SMALL DEPTH; 5 CONCLUSION | |
| 505 | 8 | |a AcknowledgementReferences; Chapter 4. Residual Algorithms: Reinforcement Learning with Function Approximation; ABSTRACT; 1 INTRODUCTION; 2 ALGORITHMS FOR LOOKUP TABLES; 3 DIRECT ALGORITHMS; 4 RESIDUAL GRADIENT ALGORITHMS; 5 RESIDUAL ALGORITHMS; 6 STOCHASTIC MDPS AND MODELS; 7 MDPS WITH MULTIPLE ACTIONS; 8 RESIDUAL ALGORITHM SUMMARY; 9 SIMULATION RESULTS; 10 CONCLUSIONS; Acknowledgments; References; Chapter 5. Removing the Genetics from the Standard Genetic Algorithm; Abstract; 1. THE GENETIC ALGORITHM (GA); 2. FOUR PEAKS: A PROBLEM DESIGNED TO BE GA-FRIENDLY; 3. SELECTING THE GA'S PARAMETERS | |
| 505 | 8 | |a 4. POPULATION-BASED INCREMENTAL LEARNING5. EMPIRICAL ANALYSIS ON THE FOUR PEAKS PROBLEM; 6. DISCUSSION; 7. CONCLUSIONS; ACKNOWLEDGEMENTS; REFERENCES; Chapter 6. Inductive Learning of Reactive Action Models; Abstract; 1 INTRODUCTION; 2 CONTEXT OF THE LEARNER; 3 ACTIONS AND TELEO-OPERATORS; 4 COLLECTING INSTANCES FOR LEARNING; 5 THE INDUCTIVE LOGIC PROGRAMMING ALGORITHM; 6 EVALUATION; 7 RELATED WORK; 8 FUTURE WORK; Acknowledgements; References; Chapter 7. Visualizing High-Dimensional Structure with the Incremental Grid Growing Neural Network; Abstract; 1 INTRODUCTION; 2 INCREMENTAL GRID GROWING | |
| 505 | 8 | |a 3 COMPARISON USING MINIMUM SPANNING TREEDATA4 DEMONSTRATION USING REALWORLD SEMANTIC DATA; 5 DISCUSSION AND FUTURE WORK; 6 CONCLUSION; References; Chapter 8. Empirical support for Winnow and Weighted-Majority based algorithms: results on a calendar scheduling domain; Abstract; 1 Introduction; 2 The learning problem; 3 Description of the algorithms; 4 Experimental results; 5 Theoretical results; Acknowledgements; References; Appendix; Chapter 9. Automatic Selection of Split Criterion during Tree Growing Based on Node Location; Abstract; 1 DECISION TREE CONSTRUCTION | |
| 520 | |a Machine Learning Proceedings 1995. | ||
| 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 Apprentissage automatique |x Congr�es. |2 ram | |
| 655 | 2 | |a Congress |0 (DNLM)D016423 | |
| 655 | 7 | |a proceedings (reports) |2 aat |0 (CStmoGRI)aatgf300027316 | |
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| 655 | 7 | |a Actes de congr�es. |2 rvmgf |0 (CaQQLa)RVMGF-000001049 | |
| 700 | 1 | |a Prieditis, Armand. | |
| 700 | 1 | |a Russell, Stuart J. |q (Stuart Jonathan), |d 1962- | |
| 776 | 0 | 8 | |i Print version: |a International Conference on Machine Learning (12th : 1995 : Tahoe City, Calif.). |t Machine learning. |d San Francisco, CA : Morgan Kaufmann Publishers, �1995 |w (OCoLC)33065822 |
| 856 | 4 | 0 | |u https://sciencedirect.uam.elogim.com/science/book/9781558603776 |z Texto completo |