Read Kernel Methods for Pattern Analysis by John Shawe-Taylor, Nello Cristianini Online
[John Shawe-Taylor, Nello Cristianini] Ú Kernel Methods for Pattern Analysis Ð Read Online eBook or Kindle ePUB. Kernel Methods for Pattern Analysis It also serves as an 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.. This book provides professionals with a large selection of algorithms, kernels and solutions ready for implementation and suitable for standard pattern discovery problems in fields such as bioinformatics,
| Title | : | Kernel Methods for Pattern Analysis |
| Author | : | |
| Rating | : | 4.62 (721 Votes) |
| Asin | : | 0511809689 |
| Format Type | : | paperback |
| Number of Pages | : | 310 Pages |
| Publish Date | : | 2013-05-03 |
| Language | : | English |
It also serves as an 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.. This book provides professionals with a large selection of algorithms, kernels and solutions ready for implementation and suitable for standard pattern discovery problems in fields such as bioinformatics, text analysis and image analysis
John Shawe Taylor is a Professor at the School of Electronics and Computer Science, University of SouthamptonNello Cristianini is a Professor of Artificial Intelligence, University of Bristol.
"Nice introduction, but no more" according to gilgamash. 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 o. coherent and accessible reference, ready-to-use algorithms 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 . THE reference work This book will, without doubt, become THE reference work in kernel methods for pattern recognition, and a must read for pattern recognition researchers and practitioners in general.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
"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 especially 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 its 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. Last, Journal of the American Statistical Association . The accompanying Matlab code or pseudocode is also extremely use
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