很经典的参考书。 1 Introduction 2 Probability Distributions 3 Linear Models for Regression 4 Linear Models for Classification 5 Neural Networks 6 Kernel Methods 7 Sparse Kernel Machines 8 Graphical Models 9 Mixture Models and EM 10 Approximate Inference 11 Sampling Methods ...

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Our purposes in writing this Second Edition | more than a quarter century after the original | remain the same: to give a systematic account of the major topics in pattern recognition, based whenever possible on fundamental principles. We believe that this provides the required foundation for solving problems in more specialized application areas such as speech recognition, optical character recognition, signal analysis, and so on. Since 1973, there has been an immense wealth of e®ort, and in many cases progress, on the topics we addressed in the First Edition. The pace of progress in algorithms for learning and pattern recognition has been exceeded only by the improvements in computer hardware. Some of the outstanding problems acknowledged in the First Edition have been solved, whereas others remain as frustrating as ever. Taken with the manifest usefulness of pattern recognition, this makes the ¯eld extremely vigorous and exciting.

本资源包含Pattern Recognition And Machine Learning的英文版和由马春鹏翻译的中文版。

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模式识别 Pattern Recognition 英文版 第四版 By Sergios Theodoridis

Information Science and Statistics Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had significant impact on both algorithms and applications.