文件名称:Neural Network Learning
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This book is about the use of artificial neural networks for supervised learning problems. Many such problems occur in practical applications of artificial neural networks. For example, a neural network might be used as a component of a face recognition system for a security appli-
cation. After seeing a number of images of legitimate users' faces, the network needs to determine accurately whether a new image corresponds to the face of a legitimate user or an imposter. In other applications, such as the prediction of future price of shares on the stock exchange, we may require a neural network to model the relationship between a pattern and a real-valued quantity.
cation. After seeing a number of images of legitimate users' faces, the network needs to determine accurately whether a new image corresponds to the face of a legitimate user or an imposter. In other applications, such as the prediction of future price of shares on the stock exchange, we may require a neural network to model the relationship between a pattern and a real-valued quantity.
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压缩包 : Neural Network Learning.rar 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