Support vector machines（支持向量机）_Andreas Christmann，Ingo Steinwart[著]_9787519296926

本书旨在解释使支持向量机(sMs)成为各种应用的成功建模和预测工具的原理。书中通过展示支持向量机的基本概念，以及最新发展和当前的研究问题来实现这一目标。本书分析了支持向量机成功的至少三个原因：它们在只有少量自由参数的情况下很好地学习的能力，它们对几种类型的模型违反和异常值的鲁棒性，最后是它们的计算效率与其他几种方法进行的比较。

　　The goal of this book is to explain the principles that made support vector machines （SVMs） a successful modeling and prediction tool for a variety of applications. We try to achieve this by presenting the basic ideas of SVMs together with the latest developments and current research questions in a unified style. In a nutshell， we identify at least three reasons for the success of SVMs： their ability to learn well with only a very small number of free parameters， their robustness against several types of model violations and outliers， and last but not least their computational efficiency compared with several other methods.

　　Although there are several roots and precursors of SVMs， these methods gained particular momentum during the last 15 years since Vapnik (1995，1998） published his well-known textbooks on statistical learning theory with a special emphasis on support vector machines. Since then， the field of machine learning has witnessed intense activity in the study of SVMs， which has spread more and more to other disciplines such as statistics and mathematics. Thus it seems fair to say that several communities are currently working on support vector machines and on related kernel-based methods. Although there are many interactions between these communities， we think that there is still room for additional fruitfulinteraction and would be glad if this textbook were found helpfulin stimulating further research. Many of the results presented in this book have previously been scattered in the journal literature or are still under review. As a consequence， these results have been accessible only to a relatively small number of specialists， sometimes probably only to people from one community but not the others. In view of the importance of SVMs for statistical machine learning， we hope that the unified presentation given here will make these results more accessible to researchers and users from different communities (e.g.; from the fields of statistics， mathematics， computer science，bioinformatics， data and text mining， and engineering）.

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