[PDF.59cw] Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)

Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)

[PDF.pm65] Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)

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 | #427223 in Books |  The MIT Press |  2001-12-15 | Original language:English | PDF # 1 |  10.00 x1.06 x8.00l,3.28 | File type: PDF | 644 pages

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||3 of 3 people found the following review helpful.| This book is very good, up to date, and provides solid explanations. Not for your novice though|By Michael Valenzuela|This book is dedicated almost entirely to support vector machines for pattern recognition. This is not really an introductory text to machine learning though. For that I would recommend Statistical Learning Theory by Vapnik or Neural Networks and Learning Machin| ||Interesting and original. Learning with Kernels will make a fine textbook on this subject. (Grace Wahba, Bascom Professor of Statistics, University of Wisconsin Madison)

|This splendid book fills the need for a comprehensive treatment of ke

In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs -- -kernels--for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replaci...

You easily download any file type for your device.Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)   |  Bernhard Schlkopf, Alexander J. Smola.Not only was the story interesting, engaging and relatable, it also teaches lessons.

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