內容大鋼
模式分析是從一批數據中尋找普遍關係的過程。它逐漸成為許多學科的核心，從生物信息學到文檔檢索都有廣泛需求。本書所描述的核方法為所有這些學科提供了一個有力的和統一的框架，推動了可以用於各種普遍形式的數據（如字元串、向量、文本等）的各種演算法的發展，並可以用於尋找各種普遍的關係類型（如排序、分類、回歸和聚類等）。書中提供了大量演算法、核函數和具體解決方案供各種實際問題選擇使用。書中描述了各種核函數，從基本的例子到高等遞歸核函數，從生成模型導出的核函數（如HMM）到基於動態規劃的串匹配核函數，以及用於處理文本文檔的特殊核函數等。本書適用於所有從事人工智慧、模式識別、機器學習、神經網路及其應用的學生、教師和研究人員，也可供相關領域的科研人員參考。
目錄
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
7.1 The smallest enclosing hypersphere
7.2 Support vector machines for classification
7.3 Support vector machines for regression
7.4 On-line classification and regression
7.5 Summary
7.6 Further reading and advanced topics
8 Ranking, clustering and data Visualisation
8.1 Discovering rank relations
8.2 Discovering cluster structure in a fleature space
8.3 Data visualisation
8.4 Summary
8.5 Further reading and advanced topics
Part Ⅲ Constructing kernels
9 Basic kernels and kernel types
9.1 Kernels in closed form
9.2 ANOVA kernels
9.3 Kernels from graphs
9.4 Diffusion kernels on graph nodes
9.5 Kernels on sets
9.6 Kernels on real numbers
9.7 Randomised kernels
9.8 Other kernel types
9.9 Summary
9.10 Further reading and advanced topics
10 Kernels for text
10.1 From bag of words to semantic space
10.2 Vactor space kernels
10.3 Summary
10.4 Further reading and advanced topics
11 Kernels for structured data: strings, trees, etc.
11.1 Comparing strings and sequences
11.2 Spectrum kernels
11.3 All-subseauences kernels
11.4 Fixed length subsequences kernels
11.5 Gap-weighted subsequences kernels
11.6 Beyond dynamic programming: trie-based kernels
11.7 Kernels for structured data
11.8 Summary
11.9 Further reading and advanced topics
12 Kernels from generative models
12.1 P-kernels
12.2 Fisher kernels
12.3 Summary
12.4 Further reading and advanced topics
Appendix A Proofs omitted from the main text
Appendix B Notational conventions
Appendix C List of pattern analysis methods
Appendix D List of kernels
References
Index
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