Neural Network Learning（I***N=9780521118620） pdf 夸克云 tct umd 下载 2025 azw3 kindle

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 questi***. 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 ***d. The authors explain the role of

scale-sensitive versi*** 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 c***tructive learning algorithms. The book is

self-contained and is intended to be accessible to researchers and

graduate students in computer science, engineering, and

mathematics.

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 dimensi***

13. The sample complexity of classification learning

14. The dimensi*** of neural networks

15. Model selection

Part III. Learning Real-Valued Functi***:

16. Learning classes of real functi***

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. C***tructive 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 ***r 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)