支持向量机（Support Vector Machine，SVM）是建立在弗拉基米尔·万普尼克（Vladimir Vapnik）提出的统计学习理论基础上的一种使用广泛的机器学习方法。这本简明导论教程对支持向量机及其理论基础进行了全面的介绍。书中从机器学习方法论讲到超平面、核函数、泛化理论、优化理论，最后总结到支持向量机理论，并介绍了其实现技术及应用。本书的叙述循序渐进，内容深入浅出，既严谨又易于理解。书中清晰的条理、富于逻辑性的推导以及优美的文字，备受初学者和专家的赞许。本书可作为计算机、自动化、电子工程、应用数学等专业的高年级本科生或研究生教材，也可作为机器学习、人工智能、神经网络、数据挖掘等课程的参考教材，同时还是相关领域的教师和研究人员的参考书。
·这本经典入门教材不仅引入了学习支持向量机所需的高等数学，更是帮助读者从直觉上理解数学公式背后的原理。
·两位英国科学家作者是国际上极富盛名的人工智能专家。
In the last few years there have been very significant developments in the theoretical understanding of Support Vector Machines （SVMs） as well as algorithmic strategies for implementing them， and applications of the approach to practical problems. We believe that the topic has reached the point at which it should perhaps be viewed as its own subfield of machine learning， a subfield which promises much in both theoretical insights and practical usefulness. Despite reaching this stage of development， we were aware that no organic integrated introduction to the subject had yet been attempted. Presenting a comprehensive introduction to SVMs requires the synthesis of a surprisingly wide range of material， including dual representations， feature spaces， learning theory， optimisation theory， and algorithmics. Though active research is still being pursued in all of these areas， there are stable foundations in each that together form the basis for the SVM concept. By building from those stable foundations， this book attempts a measured and accessible introduction to the subject of Support Vector Machines.
The book is intended for machine learning students and practitioners who want a gentle but rigorous introduction to this new class of learning systems. It is organised as a textbook that can be used either as a central text for a course on SVMs， or as an additional text in a neural networks， machine learning， or pattern recognition class. Despite its organisation as a textbook， we have kept the presentation self-contained to ensure that it is suitable for the interested scientific reader not necessarily working directly in machine learning or computer science. In this way the book should give readers from other scientific disciplines a practical introduction to Support Vector Machines enabling them to apply the approach to problems from their own domain. We have attempted to provide the reader with a route map through the rigorous derivation of the material. For this reason we have only included proofs or proof sketches where they are accessible and where we feel that they enhance the understanding of the main ideas. Readers who are interested in the detailed proofs of the quoted results are referred to the original articles.
内洛·克里斯蒂安尼尼（Nello Cristianini）目前是英国布里斯托尔大学计算机科学系的人工智能教授。他获得过英国皇家学会沃尔夫森杰出研究成就奖和欧洲研究理事会高阶研究基金奖。2014年他被汤森路透列入2002至2012十年间具影响力的科学家名单，2016年被AMiner列入机器学习领域具影响力的百位研究者名单。
约翰·肖·泰勒（John Shawe-Taylor）目前是英国伦敦大学学院联合国教科文组织人工智能讲席教授，并担任计算机科学系系主任和计算统计和机器学习中心主任。他还协调组织了多个机器学习欧洲联合研究项目，比如NeuroCOLT（“神经计算学习”）项目和PASCAL（“模式分析、统计建模与计算学习”）项目。
内洛·克里斯蒂安尼尼（Nello Cristianini）目前是英国布里斯托尔大学计算机科学系的人工智能教授。他获得过英国皇家学会沃尔夫森杰出研究成就奖和欧洲研究理事会高阶研究基金奖。2014年他被汤森路透列入2002至2012十年间最具影响力的科学家名单，2016年被AMiner列入机器学习领域最具影响力的百位研究者名单。
约翰·肖·泰勒（John Shawe-Taylor）目前是英国伦敦大学学院联合国教科文组织人工智能讲席教授，并担任计算机科学系系主任和计算统计和机器学习中心主任。他还协调组织了多个机器学习欧洲联合研究项目，比如NeuroCOLT（“神经计算学习”）项目和PASCAL（“模式分析、统计建模与计算学习”）项目。
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