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Machine learning : a theoretical approach /
This is the first comprehensive introduction to computational learning theory. The author's uniform presentation of fundamental results and their applications offers AI researchers a theoretical perspective on the problems they study. The book presents tools for the analysis of probabilistic mo...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
San Mateo, CA :
M. Kaufmann,
�1991.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Natarajan, Balas Kausik. | |
| 245 | 1 | 0 | |a Machine learning : |b a theoretical approach / |c Balas K. Natarajan. |
| 264 | 1 | |a San Mateo, CA : |b M. Kaufmann, |c �1991. | |
| 300 | |a 1 online resource (x, 217 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 (pages 207-214) and index. | ||
| 520 | |a This is the first comprehensive introduction to computational learning theory. The author's uniform presentation of fundamental results and their applications offers AI researchers a theoretical perspective on the problems they study. The book presents tools for the analysis of probabilistic models of learning, tools that crisply classify what is and is not efficiently learnable. After a general introduction to Valiant's PAC paradigm and the important notion of the Vapnik-Chervonenkis dimension, the author explores specific topics such as finite automata and neural networks. The presentation is intended for a broad audience--the author's ability to motivate and pace discussions for beginners has been praised by reviewers. Each chapter contains numerous examples and exercises, as well as a useful summary of important results. An excellent introduction to the area, suitable either for a first course, or as a component in general machine learning and advanced AI courses. Also an important reference for AI researchers. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Front Cover; Copyright Page; Machine Learning: A Theoretical Approach; Table of Contents; Chapter 1. Introduction; 1.1 Bibliographic Notes; Chapter 2. Learning Concepts on Countable Domains; 2.1 Preliminaries; 2.2 Sample Complexity; 2.3 Dimension and Learnability; 2.4 Learning Concepts with One-Sided Error; 2.5 Summary; 2.6 Appendix; 2.7 Exercises; 2.8 Bibliographic Notes; Chapter 3. Time Complexity of Concept Learning; 3.1 Preliminaries; 3.2 Polynomial-Time Learnability; 3.3 Occam's Razor; 3.4 One-Sided Error; 3.5 Hardness Results; 3.6 Summary; 3.7 Appendix; 3.8 Exercises. | |
| 505 | 8 | |a 3.9 Bibliographic NotesChapter 4. Learning Concepts on Uncountable Domains; 4.1 Preliminaries; 4.2 Uniform Convergence and Learnability; 4.3 Summary; 4.4 Appendix; 4.5 Exercises; 4.6 Bibliographic Notes; Chapter 5. Learning Functions; 5.1 Learning Functions on Countable Domains; 5.2 Learning Functions on Uncountable Domains; 5.3 Summary; 5.4 Exercises; 5.5 Bibliographic Notes; Chapter 6. Finite Automata; 6.1 Preliminaries; 6.2 A Modified Framework.; 6.3 Summary; 6.4 Exercises; 6.5 Bibliographic Notes; Chapter 7. Neural Networks; 7.1 Preliminaries; 7.2 Bounded-Precision Networks. | |
| 505 | 8 | |a 7.3 Efficiency Issues7.4 Summary; 7.5 Appendix; 7.6 Exercises; 7.7 Bibliographie Notes; Chapter 8. Generalizing the Learning Model; 8.1 Preliminaries; 8.2 Sample Complexity; 8.3 Time Complexity; 8.4 Prediction; 8.5 Boosting; 8.6 Summary; 8.7 Exercises; 8.8 Bibliographic Notes; Chapter 9. Conclusion; 9.1 The Paradigm; 9.2 Recent and Future Directions; 9.3 An AI Perspective; Index. | |
| 546 | |a English. | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 6 | |a Apprentissage automatique. |0 (CaQQLa)201-0131435 | |
| 650 | 7 | |a COMPUTERS |x General. |2 bisacsh | |
| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 650 | 7 | |a Maschinelles Lernen |2 gnd |0 (DE-588)4193754-5 | |
| 650 | 1 | 7 | |a Machine-learning. |2 gtt |
| 650 | 7 | |a Apprentissage automatique. |2 ram | |
| 776 | 0 | 8 | |i Print version: |a Natarajan, Balas Kausik. |t Machine learning |z 1558601481 |w (DLC) 91014432 |w (OCoLC)23582976 |
| 856 | 4 | 0 | |u https://sciencedirect.uam.elogim.com/science/book/9780080510538 |z Texto completo |