Learning with Kernels Support Vector Machines, Regularization,

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Authors: Bernhard Schlkopf, Alexander J. Smola Pages: 626
Publisher: MIT
ISBN13: 9780262194754
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 replacing neural networks 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 understand and apply the powerful algorithms that have been developed over the last few years.

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关键词：Machines Learning Kernels earning Support Learning Vector Kernels Support Machines

有点贵，但是下来看看

确实有点贵

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确实有点贵,就是没有币呀

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