Kernel Methods for Pattern Analysis

By: John Shawe-Taylor, Nello Cristianini

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Hardcover | 24 August 2004

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478 Pages

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Kernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e.g. strings, vectors or text) and look for general types of relations (e.g. rankings, classifications, regressions, clusters). The application areas range from neural networks and pattern recognition to machine learning and data mining. This book, developed from lectures and tutorials, fulfils two major roles: firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to use for standard pattern discovery problems in fields such as bioinformatics, text analysis, image analysis. Secondly it provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.

Industry Reviews

'Kernel methods form an important aspect of modern pattern analysis, and this book gives a lively and timely account of such methods. ... if you want to get a good idea of the current research in this field, this book cannot be ignored.' SIAM Review '... the book provides an excellent overview of this growing field. I highly recommend it to those who are interested in pattern analysis and machine learning, and especailly to those who want to apply kernel-based methods to text analysis and bioinformatics problems.' Computing Reviews ' ... I enjoyed reading this book and am happy about is addition to my library as it is a valuable practitioner's reference. I especially liked the presentation of kernel-based pattern analysis algorithms in terse mathematical steps clearly identifying input data, output data, and steps of the process. The accompanying Matlab code or pseudocode is al extremely useful.' IAPR Newsletter

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Mathematical Theory of Computation Psychology Cognition & Cognitive Psychology Artificial Intelligence Computer Vision Business Applications Mathematics Pattern Recognition Systems Analysis & Design

Databases Data Capture & Analysis

List of code fragments	p. viii
Preface	p. xi
Basic concepts	p. 1
Pattern analysis	p. 3
Patterns in data	p. 4
Pattern analysis algorithms	p. 12
Exploiting patterns	p. 17
Summary	p. 22
Further reading and advanced topics	p. 23
Kernel methods: an overview	p. 25
The overall picture	p. 26
Linear regression in a feature space	p. 27
Other examples	p. 36
The modularity of kernel methods	p. 42
Roadmap of the book	p. 43
Summary	p. 44
Further reading and advanced topics	p. 45
Properties of kernels	p. 47
Inner products and positive semi-definite matrices	p. 48
Characterisation of kernels	p. 60
The kernel matrix	p. 68
Kernel construction	p. 74
Summary	p. 82
Further reading and advanced topics	p. 82
Detecting stable patterns	p. 85
Concentration inequalities	p. 86
Capacity and regularisation: Rademacher theory	p. 93
Pattern stability for kernel-based classes	p. 97
A pragmatic approach	p. 104
Summary	p. 105
Further reading and advanced topics	p. 106
Pattern analysis algorithms	p. 109
Elementary algorithms in feature space	p. 111
Means and distances	p. 112
Computing projections: Gram-Schmidt, QR and Cholesky	p. 122
Measuring the spread of the data	p. 128
Fisher discriminant analysis I	p. 132
Summary	p. 137
Further reading and advanced topics	p. 138
Pattern analysis using eigen-decompositions	p. 140
Singular value decomposition	p. 141
Principal components analysis	p. 143
Directions of maximum covariance	p. 155
The generalised eigenvector problem	p. 161
Canonical correlation analysis	p. 164
Fisher discriminant analysis II	p. 176
Methods for linear regression	p. 176
Summary	p. 192
Further reading and advanced topics	p. 193
Pattern analysis using convex optimisation	p. 195
The smallest enclosing hypersphere	p. 196
Support vector machines for classification	p. 211
Support vector machines for regression	p. 230
On-line classification and regression	p. 241
Summary	p. 249
Further reading and advanced topics	p. 250
Ranking, clustering and data visualisation	p. 252
Discovering rank relations	p. 253
Discovering cluster structure in a feature space	p. 264
Data visualisation	p. 280
Summary	p. 286
Further reading and advanced topics	p. 286
Constructing kernels	p. 289
Basic kernels and kernel types	p. 291
Kernels in closed form	p. 292
ANOVA kernels	p. 297
Kernels from graphs	p. 304
Diffusion kernels on graph nodes	p. 310
Kernels on sets	p. 314
Kernels on real numbers	p. 318
Randomised kernels	p. 320
Other kernel types	p. 322
Summary	p. 324
Further reading and advanced topics	p. 325
Kernels for text	p. 327
From bag of words to semantic space	p. 328
Vector space kernels	p. 331
Summary	p. 341
Further reading and advanced topics	p. 342
Kernels for structured data: strings, trees, etc.	p. 344
Comparing strings and sequences	p. 345
Spectrum kernels	p. 347
All-subsequences kernels	p. 351
Fixed length subsequences kernels	p. 357
Gap-weighted subsequences kernels	p. 360
Beyond dynamic programming: trie-based kernels	p. 372
Kernels for structured data	p. 382
Summary	p. 395
Further reading and advanced topics	p. 395
Kernels from generative models	p. 397
P-kernels	p. 398
Fisher kernels	p. 421
Summary	p. 435
Further reading and advanced topics	p. 436
Proofs omitted from the main text	p. 437
Notational conventions	p. 444
List of pattern analysis methods	p. 446
List of kernels	p. 448
References	p. 450
Index	p. 460
Table of Contents provided by Ingram. All Rights Reserved.

Kernel Methods for Pattern Analysis

At a Glance

Hardcover

Industry Reviews

Shipping

How to return your order

Defective items

You Can Find This Book In

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