比较旧的一本书，但值得一读。有些论坛上可能暂时还没有，跟大家分享一下。参考文献部分缺失，美中不足，可以通过amazon预览看到一部分参考文献。

PART I: THEORY
Chapter 1. Introduction to Support Vector Learning
Chapter 2. Roadmap
Chapter 3. Three Remarks on the Support Vector Method of Function Estimation
Chapter 4. Generalization Performance of Support Vector Machines and Other Pattern Classifiers
Chapter 5. Bayesian Voting Schemes and Large Margin Classifiers (有趣)
Chapter 6. Support Vector Machines, Reproducing Kernel Hilbert Spaces, and Randomized GACV
Chapter 7. Geometry and Invariance in Kernel Based Methods (有趣)
Chapter 8. On the Annealed VC Entropy for Margin Classifiers: A Statistical Mechanics Study
Chapter 9. Entropy Numbers, Operators and Support Vector Kernels

PART II: IMPLEMENTATIONS
Chapter 10: Solving the Quadratic Programming Problem Arising in Support Vector Classification
Chapter 11: Making Large-Scale Support Vector Machine Learning Practical
Chapter 12: Fast Traning of Support Vector Machines Using Sequnential Minimal Optimization
Chapter 13: Support Vector Machines for Dynamic Reconstruction of a Chaotic System
Chapter 14: Using Support Vector Machines for Time Series Prediction
Chapter 15: Pairwise Classification and Support Vector Machines (Hm... 怀疑)
Chapter 16: Reducing hthe Run-time Complexity in SVM
Chapter 17: Support Vector Regression with ANOVA Decomposition Kernels (有趣）
Chpater 18: Support Vector Density Estimation
Chpater 19: Combining Support Vevtor and Mathematical Programming Methods for Classification
Chapter 20: Kernel Principal Component Analysis (有趣)