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.

Learning with kernels: support vector machines, regularization, optimization, and beyond PDF

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