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Machine Learning
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ID: 176842
Balas Natarajan
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This is the first comprehensive introduction to computational learning theory. The author's uniform. AI researcher. The book offers tools for the analysis of probabilistic models of learning. After a general introduction to Valiant's PAC paradigm and the important notion of the Vapnik-Chervonenkis dimension, the author explores the specific issues of such finite automata and neural networks. The presentation is intended for a broad audience - the author's ability to be motivated and speeches for beginners has been praised by reviewers. Each chapter contains numerous examples and exercises. An excellent introduction to the area, suitable for the first course, or as a component in general machine learning and advanced AI courses. Also an important reference for AI researchers.



Chapter 1 Introduction
Chapter 2 Learning Concept on Countable Domains
Chapter 3 Time Complexity of Learning Concept
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
176842

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