@article{1387241,
      recid = {1387241},
      author = {Schölkopf, Bernhard. and Smola, Alexander J.},
      title = {Learning with kernels : support vector machines,  regularization, optimization, and beyond /},
      publisher = {MIT Press,},
      address = {Cambridge, Mass. :},
      pages = {1 online resource (xviii, 626 pages) :},
      year = {2002},
      abstract = {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.},
      url = {http://library.usi.edu/record/1387241},
}