For those entering the field of artificial neural networks, there has been an acute need for an authoritative textbook that explains the main ideas clearly and consistently using the basic tools of linear algebra, calculus, and simple probability theory. There have been many attempts to provide such a text, but until now, none has succeeded. Some authors have failed to separate the basic ideas and principles from the soft and fuzzy intuitions that led to some of the models as well as to most of the exaggerated claims. Others have been unwilling to use the basic mathematical tools that are essential for a rigorous understanding of the material. Yet others have tried to cover too many different kinds of neural network without going into enough depth on any one of them. The most successful attempt to date has been "Introduction to the Theory of Neural Computation" by Hertz, Krogh and Palmer. Unfortunately, this book started life as a graduate course in statistical physics and it shows. So despite its many admirable qualities it is not ideal as a general textbook.

A tutorial on support vector machines for pattern recognition 的原文和对应的中文翻译 是SVM最为经典的资料之一

这本模式识别的书非常经典，然而也非常稀罕，很少有人有。我最早读的是第二版，这本书全面覆盖了统计模式识别领域的重要知识点。书中用大量篇幅讲解无监督聚类方法，这一点在模式识别教材中应该是独一无二的，比如Duda的书在这方面只留了一章，处理的也比较简单。另外，本书还有章节专门讲特征抽取、选择，以及模板匹配这些内容，也弥补了Duda教材的不足。第三版增加了一些内容，主要是基于核方法的内容，反映了学界的进展。-Book of this pattern recognition is very classic, however, very rare, very few people have. I first read the second edition, this book comprehensively covers the important points of the field of statistical pattern recognition. Book at great length to explain the unsupervised clustering method, which is unique in pattern recognition textbooks, such as Duda' s book in this regard, leaving only one chapter, is also relatively simple to deal with. In addition, the book there are chapters dedicated speakers feature extraction, selection, and template matching, but also compensate for the lack of materials Duda. The third edition of the increase in some of the content is mainly based on the content of the kernel method, reflecting the academic progress.

模式识别第四版答案（pattern recognition fourth edition solution）