支持向量机与基于核的机器学习导论(英文版)

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 。

Preface Notation 1 The Learning Methodology 1.1 Supervised Learning 1.2 Learning and Generalisation 1.3 Improving Generalisation 1.4 Attractions and Drawbacks of Learning 1.5 Support Vector Machines for Learning 1.6 Exercises 1.7 Further Reading and Advanced Topics 2 Linear Learning Machines 2.1 Linear Classification 2.1.1 Rosenblatt's Perceptron 2.1.2 Other Linear Classmers 2.1.3 Multi-class Discfimination 2.2 Linear Regression 2.2.1 Least Squares 2.2.2 Ridge Regression 2.3 Dual Representation of Linear Machines 2.4 Exercises 2.5 Further Reading and Advanced Topics 3 Kernel-Induced Fleature Spaces 3.1 Learning jn Feature Space 3.2 The Implicit Mapping into Feature Space 3.3 Making Kernels 3.3.1 Characterisation of Kernels 3.3.2 Making Kernels from Kernels 3.3.3 Making Kernels from Features 3.4 Working in Feature Space 3.5 Kernels and Gaussian Processes 3.6 Exercises 3.7 Further Reading and Advanced Topics 4 Generalisation Theory 4.1 Probably Approximately Correct Learning 4.2 Vapnik Chenronenkis (VC) Theory 4.3 Margin-Based Bounds on Generalisation 4.3.1 Maximal Margin Bounds 4.3.2 Margin Percentile Bounds 4.3.3 Soft Margin Bounds 4.4 Other Bounds on Generalisation and Luckiness 4.5 Generalisation for Regression 4.6 Bayesian Analysis of Learning 4.7 Exercises 4.8 Further Reading and Advanced Topics 5 Optimisation Theory 5.1 Problem Formulation 5.2 Lagrangian Theory 5.3 Duality 5.4 Exercises 5.5 Further Reading and Advanced Topics