Kernel Methods for Pattern Analysis (英語) ハードカバー – 2004/6/28
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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.
'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
I recently discovered that kernel methods are valuable tools for solving classification problems in a nearly optimal way. Apparently they are also useful for regression.
This is the third textbook that I purchased for the purpose of understanding kernel methods. I have scarcely encountered a more elegantly written text. It does a superb job of building intuition and is also mathematically rigorous. Such texts are rare.
This is the first textbook that I rely on when it comes to kernel methods.
I also have the book Learning with Kernels, (Scholkopf and Smola) but I found it harder to follow and fragmented in their presentation.
It is theoretically well-founded, the resulting algorithms are well-explained and made accessible for practioners by providing pseudo-code and online, ready-to-use matlab code.
This book nicely complements the previous, yellow book, written by the same authors. Indeed, after "getting into the field" by reading the accessible introduction to support vector machines (SVMs), it was clear to me that SVMs was only an example of a signifcantly larger framework, i.e., kernel methods. The blue book is the reference book about that larger framework I have been waiting for since then. I particularly like the way the book is set up, making clear the modular, flexible approach in kernel methods.
It's built up in a nicely modular, accessible and didactive way, helping the reader understand thoroughly what kernel methods are all about and importantly, how to use them. This makes the book very useful as a cook book for practitioners, as well as a text book for students.
The book covers all the relevant topics in the state of the art of kernel methods, a field of research in which the authors have been a driving force since the beginning. Even so, they managed to resist the temptation from squeezing in the(ir) latest (potentially still unstable) results, which greatly enhances the timelessness of the book.