- 模式分类的支持向量机(第2版英文版香农信息科学经典)
- - 作者：(日)阿部重夫|责编:陈亮//刘叶青
  - 出版社：世图出版公司
  - ISBN：9787519296933
  - 出版日期：2023/01/01
  - 页数：471
- 售价：47.6

内容大纲
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。
作者介绍
目录
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 Privileged Information
  4.9  Semi-Supervised Learning
  4.10  Multiple Classifier Systems
  4.11  Multiple Kernel Learning
  4.12  Confidence Level

  4.13  Visualization
  References
5  Training Methods
  5.1  Preselecting Support Vector Candidates
    5.1.1  Approximation of Boundary Data
    5.1.2  Performance Evaluation
  5.2  Decomposition Techniques
  5.3  KKT Conditions Revisited
  5.4  Overview of Training Methods
  5.5  Primal Dual Interior-Point Methods
    5.5.1  Primal-Dual Interior-Point Methods for Linear Programming
    5.5.2  Primal-Dual Interior-Point Methods for Quadratic Programming
    5.5.3  Performance Evaluation
  5.6  Steepest Ascent Methods and Newton's Methods
    5.6.1  Solving Quadratic Programming Problems Without Constraints
    5.6.2  Training of L1 Soft-Margin Support Vector Machines
    5.6.3  Sequential Minimal Optimization
    5.6.4  Training of L2 Soft-Margin Support Vector Machines
    5.6.5  Performance Evaluation
  5.7  Batch Training by Exact Incremental Training
    5.7.1  KKT Conditions
    5.7.2  Training by Solving a Set of Linear Equations
    5.7.3  Performance Evaluation
  5.8  Active Set Training in Primal and Dual
    5.8.1  Training Support Vector Machines in the Primal
    5.8.2  Comparison of Training Support Vector Machines in the Primal and the Dual
    5.8.3  Performance Evaluation
  5.9  Training of Linear Programming Support Vector Machines
    5.9.1  Decomposition Techniques
    5.9.2  Decomposition Techniques for Linear Programming Support Vector Machines
    5.9.3  Computer Experiments
  References
6  Kernel-Based Methods
  6.1  Kernel Least Squares
    6.1.1  Algorithm
    6.1.2  Performance Evaluation
  6.2  Kernel Principal Component Analysis
  6.3  Kernel Mahalanobis Distance
    6.3.1  SVD-Based Kernel Mahalanobis Distance
    6.3.2  KPCA-Based Mahalanobis Distance
  6.4  Principal Component Analysis in the Empirical Feature Space
  6.5  Kernel Discriminant Analysis
    6.5.1  Kernel Discriminant Analysis for Two-Class Problems
    6.5.2  Linear Discriminant Analysis for Two-Class Problems in the Empirical Feature Space
    6.5.3  Kernel Discriminant Analysis for Multiclass Problems
  References
7  Feature Selection and Extraction
  7.1  Selecting an Initial Set of Features
  7.2  Procedure for Feature Selection
  7.3  Feature Selection Using Support Vector Machines

    7.3.1  Backward or Forward Feature Selection
    7.3.2  Support Vector Machine-Based Feature Selection
    7.3.3  Feature Selection by Cross-Validation
  7.4  Feature Extraction
  References
8  Clustering
  8.1  Domain Description
  8.2  Extension to Clustering
  References
9  Maximum-Margin Multilayer Neural Networks
  9.1  Approach
  9.2  Three-Layer Neural Networks
  9.3  CARVE Algorithm
  9.4  Determination of Hidden-Layer Hyperplanes
    9.4.1  Rotation of Hyperplanes
    9.4.2  Training Algorithm
  9.5  Determination of Output-Layer Hyperplanes
  9.6  Determination of Parameter Values
  9.7  Performance Evaluation
  References
10  Maximum-Margin Fuzzy Classifiers
  10.1  Kernel Fuzzy Classifiers with Ellipsoidal Regions
    10.1.1  Conventional Fuzzy Classifiers with Ellipsoidal Regions
    10.1.2  Extension to a Feature Space
    10.1.3  Transductive Training
    10.1.4  Maximizing Margins
    10.1.5  Performance Evaluation
  10.2  Fuzzy Classifiers with Polyhedral Regions
    10.2.1  Training Methods
    10.2.2  Performance Evaluation
  References
11  Function Approximation
  11.1  Optimal Hyperplanes
  11.2  L1 Soft-Margin Support Vector Regressors
  11.3  L2 Soft-Margin Support Vector Regressors
  11.4  Model Selection
  11.5  Training Methods
    11.5.1  Overview
    11.5.2  Newton's Methods
    11.5.3  Active Set Training
  11.6  Variants of Support Vector Regressors
    11.6.1  Linear Programming Support Vector Regressors
    11.6.2  v-Support Vector Regressors
    11.6.3  Least-Squares Support Vector Regressors
  11.7  Variable Selection
    11.7.1  Overview
    11.7.2  Variable Selection by Block Deletion
    11.7.3  Performance Evaluation
  References
A  Conventional Classifiers

  A.1  Bayesian Classifiers
  A.2  Nearest-Neighbor Classifiers
  References
B  Matrices
  B.1  Matrix Properties
  B.2  Least-Squares Methods and Singular Value Decomposition
  B.3  Covariance Matrices
  References
C  Quadratic Programming
  C.1  Optimality Conditions
  C.2  Properties of Solutions
D  Positive Semidefinite Kernels and Reproducing Kernel Hilbert Space
  D.1  Positive Semidefinite Kernels
  D.2  Reproducing Kernel Hilbert Space
  References
Index

内容大纲

作者介绍

目录

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