상세정보

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.

정보제공 :

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

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