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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond Издательство: The MIT Press, 2001 г Суперобложка, 644 стр ISBN 0262194759 инфо 3728n.

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 learninащшьъg 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 replacing neural бйбжпnetworks in a variety of fields, including engineering, information retrieval, and bioinformatics Learning with Kernels provides an introduction to SVMs and related kernel methods Although the book begins with the basics, it also includes the latest research It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to undeбрпуцrstand and apply the powerful algorithms that have been developed over the last few years 1st edition Авторы Бернард Шолкопф Bernhard Schlkopf Александер Дж Смола Alexander J Smola.


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