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Neural Network Learning（ISBN=9780521118620）书籍详细信息
- ISBN：9780521118620
- 作者：暂无作者
- 出版社：暂无出版社
- 出版时间：2009-01
- 页数：暂无页数
- 价格：217.80
- 纸张：胶版纸
- 装帧：平装
- 开本：32开
- 语言：未知
- 丛书：暂无丛书
- TAG：暂无
- 豆瓣评分：暂无豆瓣评分
内容简介：
First published in 1999, this book describes theoretical
advances in the study of artificial neural networks. It explores
probabilistic models of supervised learning problems, and addresses
the key statistical and computational questions. Research on
pattern classification with binary-output networks is surveyed,
including a discussion of the relevance of the Vapnik-Chervonenkis
dimension, and calculating estimates of the dimension for several
neural network models. A model of classification by real-output
networks is developed, and the usefulness of classification with a
'large margin' is demonstrated. The authors explain the role of
scale-sensitive versions of the Vapnik-Chervonenkis dimension in
large margin classification, and in real prediction. They also
discuss the computational complexity of neural network learning,
describing a variety of hardness results, and outlining two
efficient constructive learning algorithms. The book is
self-contained and is intended to be accessible to researchers and
graduate students in computer science, engineering, and
mathematics.
书籍目录：
1. Introduction
Part I. Pattern Recognition with Binary-output Neural
Networks:
2. The pattern recognition problem
3. The growth function and VC-dimension
4. General upper bounds on sample complexity
5. General lower bounds
6. The VC-dimension of linear threshold networks
7. Bounding the VC-dimension using geometric techniques
8. VC-dimension bounds for neural networks
Part II. Pattern Recognition with Real-output Neural
Networks:
9. Classification with real values
10. Covering numbers and uniform convergence
11. The pseudo-dimension and fat-shattering dimension
12. Bounding covering numbers with dimensions
13. The sample complexity of classification learning
14. The dimensions of neural networks
15. Model selection
Part III. Learning Real-Valued Functions:
16. Learning classes of real functions
17. Uniform convergence results for real function classes
18. Bounding covering numbers
19. The sample complexity of learning function classes
20. Convex classes
21. Other learning problems
Part IV. Algorithmics:
22. Efficient learning
23. Learning as optimisation
24. The Boolean perceptron
25. Hardness results for feed-forward networks
26. Constructive learning algorithms for two-layered networks.
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编辑推荐
Contains results that have not appeared in journal papers or
other books ? Presents many recent results in a unified framework
and, in many cases, with simpler proofs ? Self-contained: it
introduces the necessary background material on probability,
statistics, combinatorics and computational complexity ? It is
suitable for graduate students as well as active researchers in the
area (parts of it have already formed the basis of a graduate
course)
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