文件名称:Neural_Network_Learning
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基于人工神经网络技术解决诸如人脸识别等模式识别问题，提供了一些快速算法，适用于应用和研究。-In this book, we concentrate on statistical and computational ques-
tions associated with the use of rich function classes, such as artificial
neural networks, for pattern recognition and prediction problems.
tions associated with the use of rich function classes, such as artificial
neural networks, for pattern recognition and prediction problems.
相关搜索： 神经网络 人脸
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下载文件列表
Bibliography .pdf
Preface .pdf
1 - Introduction .pdf
2 - The Pattern Classification Problem .pdf
3 - The Growth Function and VC-Dimension .pdf
4 - General Upper Bounds on Sample Complexity .pdf
5 - General Lower Bounds on Sample Complexity .pdf
6 - The VC-Dimension of Linear Threshold Networks .pdf
7 - Bounding the VC-Dimension using Geometric Techniques .pdf
8 - Vapnik-Chervonenkis Dimension Bounds for Neural Networks .pdf
9 - Classification with Real-Valued Functions .pdf
10 - Covering Numbers and Uniform Convergence .pdf
11 - The Pseudo-Dimension and Fat-Shattering Dimension .pdf
12 - Bounding Covering Numbers with Dimensions .pdf
13 - The Sample Complexity of Classification Learning .pdf
14 - The Dimensions of Neural Networks.pdf
15 - Model Selection .pdf
16 - Learning Classes of Real Functions .pdf
17 - Uniform Convergence Results for Real Function Classes .pdf
18 - Bounding Covering Numbers .pdf
19 - Sample Complexity of Learning Real Function Classes .pdf
20 - Convex Classes .pdf
21 - Other Learning Problems .pdf
22 - Efficient Learning .pdf
23 - Learning as Optimization .pdf
24 - The Boolean Perceptron .pdf
25 - Hardness Results for Feed-Forward Networks .pdf
26 - Constructive Learning Algorithms for Two-Layer Networks.pdf
Appendix 1 - Useful Results .pdf
notes.txt
Preface .pdf
1 - Introduction .pdf
2 - The Pattern Classification Problem .pdf
3 - The Growth Function and VC-Dimension .pdf
4 - General Upper Bounds on Sample Complexity .pdf
5 - General Lower Bounds on Sample Complexity .pdf
6 - The VC-Dimension of Linear Threshold Networks .pdf
7 - Bounding the VC-Dimension using Geometric Techniques .pdf
8 - Vapnik-Chervonenkis Dimension Bounds for Neural Networks .pdf
9 - Classification with Real-Valued Functions .pdf
10 - Covering Numbers and Uniform Convergence .pdf
11 - The Pseudo-Dimension and Fat-Shattering Dimension .pdf
12 - Bounding Covering Numbers with Dimensions .pdf
13 - The Sample Complexity of Classification Learning .pdf
14 - The Dimensions of Neural Networks.pdf
15 - Model Selection .pdf
16 - Learning Classes of Real Functions .pdf
17 - Uniform Convergence Results for Real Function Classes .pdf
18 - Bounding Covering Numbers .pdf
19 - Sample Complexity of Learning Real Function Classes .pdf
20 - Convex Classes .pdf
21 - Other Learning Problems .pdf
22 - Efficient Learning .pdf
23 - Learning as Optimization .pdf
24 - The Boolean Perceptron .pdf
25 - Hardness Results for Feed-Forward Networks .pdf
26 - Constructive Learning Algorithms for Two-Layer Networks.pdf
Appendix 1 - Useful Results .pdf
notes.txt
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