vapnik的SVM论文 支持向量网络是一种针对两类问题的新学习机器.它的实现基于以下思想:将输入向量非线性地映射到一个很高维的特征空间.并在该特征空间中构造一个线性决策平面.该决策平面的特殊性质保证了学习机器具有很好的推广能力.支持向量网络的思想已在完全可分的训练数据集上得以实现,这里我们将它扩展到不完全可分的训练数据集

different phase in the way computer networks were used. When the first edition appeared in 1980, networks were an academic curiosity. When the second edition appeared in 1988, networks were used by universities and large businesses. When the third edition appeared in 1996, computer networks, especially the Internet, had become a daily reality for millions of people. By the fourth edition, in 2003, wireless networks and mobile computers had become commonplace for accessing the Web and the Internet. Now, in the fifth edition, networks are about content distribution (especially videos using CDNs and peer-to-peer networks) and mobile phones are small computers on the Internet. New in the Fifth Edition Among the many changes in this book, the most important one is the addition of Prof. David J. Wetherall as a co-author. David brings a rich background in networking, having cut his teeth designing metropolitan-area networks more than 20 years ago. He has worked with the Internet and wireless networks ever since and is a professor at the University of Washington, where he has been teaching and doing research on computer networks and related topics for the past decade. Of course, the book also has many changes to keep up with the: ever-changing world of computer networks. Among these are revised and new material on Wireless networks (802.12 and 802.16) The 3G networks used by smart phones RFID and sensor networks Content distribution using CDNs Peer-to-peer networks Real-time media (from stored, streaming, and live sources) Internet telephony (voice over IP) Delay-tolerant networks

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In response to the exponentially increasing need to analyze vast amounts of data, Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition provides scientists with a simple but systematic introduction to neural networks. Beginning with an introductory discussion on the role of neural networks in scientific data analysis, this book provides a solid foundation of basic neural network concepts. It contains an overview of neural network architectures for practical data analysis followed by extensive step-by-step coverage on linear networks, as well as, multi-layer perceptron for nonlinear prediction and classification explaining all stages of processing and model development illustrated through practical examples and case studies. Later chapters present an extensive coverage on Self Organizing Maps for nonlinear data clustering, recurrent networks for linear nonlinear time series forecasting, and other network types suitable for scientific data analysis. With an easy to understand format using extensive graphical illustrations and multidisciplinary scientific context, this book fills the gap in the market for neural networks for multi-dimensional scientific data, and relates neural networks to statistics. Features x Explains neural networks in a multi-disciplinary context x Uses extensive graphical illustrations to explain complex mathematical concepts for quick and easy understanding ? Examines in-depth neural networks for linear and nonlinear prediction, classification, clustering and forecasting x Illustrates all stages of model development and interpretation of results, including data preprocessing, data dimensionality reduction, input selection, model development and validation, model uncertainty assessment, sensitivity analyses on inputs, errors and model parameters Sandhya Samarasinghe obtained her MSc in Mechanical Engineering from Lumumba University in Russia and an MS and PhD in Engineering from Virginia Tech, USA.

Neural Networks - A Comprehensive Foundation

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贝叶斯网络经典教材都涵盖在这里面了，欢迎大家使用~！

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Wei Ren和Yongcan Cao关于多智能体系统分布式协调控制方向的经典教材。