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Kernel Methods for Pattern Analysis
をはじめとする140万冊以上の本をご利用いただけます。 詳細はこちら
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内容説明
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.
Book Description
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 be implemented, suitable for standard pattern discovery problems in field 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.
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5つ星のうち 4.1
8件のカスタマーレビュー
34 人中、29人の方が、｢このレビューが参考になった｣と投票しています。
5つ星のうち 5.0
coherent and accessible reference, ready-to-use algorithms
2005/2/22
By machine learning guru
- (Amazon.com)
形式:ハードカバー
This work presents a coherent overview of an important field in machine learning. The unifying framework of kernel methods has proven state of the art results and the community has been waiting for a book like this to make both theory and practice of kernel methods accesssible for readers of all different backgrounds (researchers, students, practioners from both academia and industry, ...).
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 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.
25 人中、21人の方が、｢このレビューが参考になった｣と投票しています。
5つ星のうち 2.0
Nice introduction, but no more
2006/8/11
By gilgamash
- (Amazon.com)
形式:ハードカバー
Well, at first I was petrified to find a book that sounded like it deeply explores the subject of kernel methods. But all in all, it did not quite achieve what I hoped for. As a practical approach, when it comes to implementation, it serves nicely as a reference. The deeper mathematical roots of kernels (especially when it comes to measure theory and functional analysis) are not dealt with at all or just scratched at the very surface. The notation is sometimes awkward, mentioning for example the representation of an object in a given vector space with respect to the basis. And: Too much copied and pasted from the former book about SVMs. Basically, reading papers of Carmeli, Aronszajn and others will give you a much deeper insight into the subject.
29 人中、23人の方が、｢このレビューが参考になった｣と投票しています。
5つ星のうち 5.0
A Useful Reference on Kernel Methods
2005/2/22
By Data Miner
- (Amazon.com)
形式:ハードカバー
The book is divided into 3 parts. The theory is all in part I,
the rest of the book is a cook-book with plenty of matlab code.
The website contains most of the same code + data online. Readable, complete.
the rest of the book is a cook-book with plenty of matlab code.
The website contains most of the same code + data online. Readable, complete.
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