模式分析的核方法(英文版)

模式分析是从一批数据 中寻找普遍关系的过程。它 逐渐成为许多学科的核心， 从生物信息学到文档检索都 有广泛需求。本书所描述的 核方法为所有这些学科提供 了一个有力的和统一的框架 ，推动了可以用于各种普遍 形式的数据（如字符串、向 量、文本等）的各种算法的 发展，并可以用于寻找各种 普遍的关系类型（如排序、 分类、回归和聚类等）。书 中提供了大量算法、核函数 和具体解决方案供各种实际 问题选择使用。书中描述了 各种核函数，从基本的例子 到高等递归核函数，从生成 模型导出的核函数（如 HMM）到基于动态规划的 串匹配核函数，以及用于处 理文本文档的特殊核函数等 。本书适用于所有从事人工 智能、模式识别、机器学习 、神经网络及其应用的学生 、教师和研究人员，也可供 相关领域的科研人员参考。

List of code fragments Preface Part Ⅰ Basic concepts 1 Pattern analysis 1.1 Patterns in data 1.2 Pattern analysis algorithms 1.3 Exploiting patterns 1.4 Summary 1.5 Further reading and advanced topics 2 Kernel methods: an overview 2.1 The overall picture 2.2 Linear regression in a feature space 2.3 Other examples 2.4 The modularity of kernel methods 2.5 Roadmap of the book 2.6 Summary 2.7 Further reading and advanced topics 3 Properties of kernels 3.1 Inner products and positive semi-definite matrices 3.2 Characterisation of kernels 3.3 The kernel matrix 3.4 Kernel construction 3.5 Summary 3.6 Further reading and advanced topics 4 Detecting stable patterns 4.1 Concentration inequalities 4.2 Capacity and regularisation: Rademacher theory 4.3 Pattern stability for kernel-based classes 4.4 A pragmatic approach 4.5 Summary 4.6 Further reading and advanced topics Part Ⅱ Pattern analysis algorithms 5 Elementary algorithms in feature space 5.1 Means and distances 5.2 Computing projections: Gram-Schmidt, QR and Cholesky 5.3 Measuring the spread of the data 5.4 Fisher discriminant analysis Ⅰ 5.5 Summary 5.6 Further reading and advanced topics 6 Pattern analysis using eigen-decompositions 6.1 Singular value decomposition 6.2 Principal components analysis 6.3 Directions of maximum covariance 6.4 The generalised eigenvector problem 6.5 Canonical correlation analysis 6.6 Fisher discriminant analysis Ⅱ 6.7 Methods for linear regression 6.8 Summary 6.9 Further reading and advanced topics 7 Pattern analysis using convex Optimisation