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模式分析的核方法
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作者John Shawe-Taylor；Nello Cristianini
出版社机械工业出版社
出版时间2005-01
装帧平装
货号A12
上书时间2024-12-11
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图书标准信息
- 作者 John Shawe-Taylor；Nello Cristianini
- 出版社 机械工业出版社
- 出版时间 2005-01
- ISBN 9787111155553
- 定价 59.00元
- 装帧 平装
- 开本 其他
- 纸张 其他
- 页数 1页
- 【内容简介】
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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，从神经网络到所谓句法模式识别，从统计模式识别到机器学习和数据挖掘，模式分析的应用覆盖了从生物信息学到文档检索的广泛领域。
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)。
本书有两个主要目的。首先，它为专业人员提供了一个包容广泛的工具箱，其中包含各种易于实现的算法、核函数 - 【目录】
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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 feature 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 Vector 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-subsequences 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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