Machine Learning
A Theoretical Approach
Seiten
1991
Morgan Kaufmann Publishers In (Verlag)
978-1-55860-148-2 (ISBN)
Morgan Kaufmann Publishers In (Verlag)
978-1-55860-148-2 (ISBN)
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This is an introduction to computational learning theory. The author's 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.
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.
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.
By Balas K. Natarajan
Chapter 1 Introduction
Chapter 2 Learning Concept on Countable Domains
Chapter 3 Time Complexity of Concept Learning
Chapter 4 Learning Concepts on Uncoutable Domains
Chapter 5 Learning Functions
Chapter 6 Finite Automata
Chapter 7 Neural Networks
Chapter 8 Generalizing the Learning Model
Chapter 9 Conclusion
| Erscheint lt. Verlag | 1.9.1991 |
|---|---|
| Verlagsort | San Francisco |
| Sprache | englisch |
| Gewicht | 510 g |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| ISBN-10 | 1-55860-148-1 / 1558601481 |
| ISBN-13 | 978-1-55860-148-2 / 9781558601482 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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