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...
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| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Saint Louis
Elsevier Science
2014
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| Subjects: | |
| Online Access: | FAW01 |
| Summary: | 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 |
| Item Description: | Description based on publisher supplied metadata and other sources |
| Physical Description: | 1 online resource (228 pages) |
| ISBN: | 9780080510538 9781493305858 |
Staff View
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| spelling | Natarajan, Balas K. Verfasser aut Machine Learning A Theoretical Approach Saint Louis Elsevier Science 2014 © 1991 1 online resource (228 pages) txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources 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 Machine learning Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s 1\p DE-604 Erscheint auch als Druck-Ausgabe Natarajan, Balas K . Machine Learning : A Theoretical Approach 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Natarajan, Balas K. Machine Learning A Theoretical Approach Machine learning Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4193754-5 |
| title | Machine Learning A Theoretical Approach |
| title_auth | Machine Learning A Theoretical Approach |
| title_exact_search | Machine Learning A Theoretical Approach |
| title_full | Machine Learning A Theoretical Approach |
| title_fullStr | Machine Learning A Theoretical Approach |
| title_full_unstemmed | Machine Learning A Theoretical Approach |
| title_short | Machine Learning |
| title_sort | machine learning a theoretical approach |
| title_sub | A Theoretical Approach |
| topic | Machine learning Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Machine learning Maschinelles Lernen |
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