本书是一本关于在模式分类中使用支持向量机的指南，包括对分类器和回归器的严格的性能比较。本书为多类分类和函数逼近问题、分类器和回归器的评价标准提出了架构。
本书特色：阐明了两类支持向量机的特征；讨论了提高神经网络和模糊系统泛化能力的核方法；大量的插图和例子；使用公开数据集进行性能评估；检验马氏核、经验特征空间，并通过交叉验证确定模型选择的影响；稀疏支持向量机、使用特权信息学习、半监督学习、多分类器系统和多核学习；探讨了基于增量训练的批量训练和主动集训练方法，以及线性规划支持向量机的分解技术。

Preface
Acknowledgments
Symbols
1 Introduction
1.1 Decision Functions
1.1.1 Decision Functions for Two-Class Problems
1.1.2 Decision Functions for Multiclass Problems
1.2 Determination of Decision Functions
1.3 Data Sets Used in the Book
1.4 Classifier Evaluation
References
2 Two-Class Support Vector Machines
2.1 Hard-Margin Support Vector Machines
2.2 L1 Soft-Margin Support Vector Machines
2.3 Mapping to a High-Dimensional Space
2.3.1 Kernel Tricks
2.3.2 Kernels
2.3.3 Normalizing Kernels
2.3.4 Properties of Mapping Functions Associated with Kernels
2.3.5 Implicit Bias Terms
2.3.6 Empirical Feature Space
2.4 L2 Soft-Margin Support Vector Machines
2.5 Advantages and Disadvantages
2.5.1 Advantages
2.5.2 Disadvantages
2.6 Characteristics of Solutions
2.6.1 Hessian Matrix
2.6.2 Dependence of Solutions on C
2.6.3 Equivalence of L1 and L2 Support Vector Machines
2.6.4 Nonunique Solutions
2.6.5 Reducing the Number of Support Vectors
2.6.6 Degenerate Solutions
2.6.7 Duplicate Copies of Data
2.6.8 Imbalanced Data
2.6.9 Classification for the Blood Cell Data
2.7 Class Boundaries for Different Kernels
2.8 Developing Classifiers
2.8.1 Model Selection
2.8.2 Estimating Generalization Errors
2.8.3 Sophistication of Model Selection
2.8.4 Effect of Model Selection by Cross-Validation
2.9 Invariance for Linear Transformation
References
3 Multiclass Support Vector Machines
3.1 One-Against-All Support Vector Machines
3.1.1 Conventional Support Vector Machines
3.1.2 Fuzzy Support Vector Machines
3.1.3 Equivalence of Fuzzy Support Vector Machines and Support Vector Machines with Continuous Decision Functions
3.1.4 Decision-Tree-Based Support Vector Machines
3.2 Pairwise Support Vector Machines
3.2.1 Conventional Support Vector Machines
3.2.2 Fuzzy Support Vector Machines
3.2.3 Performance Comparison of Fuzzy Support Vector Machines
3.2.4 Cluster-Based Support Vector Machines
3.2.5 Decision-Tree-Based Support Vector Machines
3.2.6 Pairwise Classification with Correcting Classifiers
3.3 Error-Correcting Output Codes
3.3.1 Output Coding by Error-Correcting Codes
3.3.2 Unified Scheme for Output Coding
3.3.3 Equivalence of ECOC with Membership Functions
3.3.4 Performance Evaluation
3.4 All-at-Once Support Vector Machines
3.5 Comparisons of Architectures
3.5.1 One-Against-All Support Vector Machines
3.5.2 Pairwise Support Vector Machines
3.5.3 ECOC Support Vector Machines
3.5.4 All-at-Once Support Vector Machines
3.5.5 Training Difficulty
3.5.6 Training Time Comparison
References
4 Variants of Support Vector Machines
4.1 Least-Squares Support Vector Machines
4.1.1 Two-Class Least-Squares Support Vector Machines
4.1.2 One-Against-All Least-Squares Support Vector Machines
4.1.3 Pairwise Least-Squares Support Vector Machines
4.1.4 All-at-Once Least-Squares Support Vector Machines
4.1.5 Performance Comparison
4.2 Linear Programming Support Vector Machines
4.2.1 Architecture
4.2.2 Performance Evaluation
4.3 Sparse Support Vector Machines
4.3.1 Several Approaches for Sparse Support Vector Machines
4.3.2 Idea
4.3.3 Support Vector Machines Trained in the Empirical Feature Space
4.3.4 Selection of Linearly Independent Data
4.3.5 Performance Evaluation
4.4 Performance Comparison of Different Classifiers
4.5 Robust Support Vector Machines
4.6 Bayesian Support Vector Machines
4.6.1 One-Dimensional Bayesian Decision ~nctions
4.6.2 Parallel Displacement of a Hyperplane
4.6.3 Normal Test
4.7 Incremental Training
4.7.1 Overview
4.7.2 Incremental Training Using Hyperspheres
4.8 Learning Using Pri