{"product_id":"learning-with-kernels-support-vector-machines-regularization-optimization-and-beyond-paperback","title":"Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond - Paperback","description":"\u003cp\u003eby \u003cb\u003eBernhard Scholkopf\u003c\/b\u003e (Author), \u003cb\u003eAlexander J. Smola\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003cb\u003eA comprehensive introduction to Support Vector Machines and related kernel methods.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eIn 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.\u003c\/p\u003e\u003cp\u003e\u003ci\u003eLearning with Kernels\u003c\/i\u003e 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.\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003eBernhard Schkopf is Director at the Max Planck Institute for Intelligent Systems in T?ingen, Germany. He is coauthor of \u003ci\u003eLearning with Kernels\u003c\/i\u003e (2002) and is a coeditor of \u003ci\u003eAdvances in Kernel Methods: Support Vector Learning\u003c\/i\u003e (1998), \u003ci\u003eAdvances in Large-Margin Classifiers\u003c\/i\u003e (2000), and \u003ci\u003eKernel Methods in Computational Biology\u003c\/i\u003e (2004), all published by the MIT Press. \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003eAlexander J. Smola is Senior Principal Researcher and Machine Learning Program Leader at National ICT Australia\/Australian National University, Canberra.\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 648\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.3 x 10 x 8 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e June 05, 2018\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":42096609165447,"sku":"9780262536578","price":162.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0601\/2623\/2711\/files\/e67d95ca06dc98ff5cffae881b2eec71.webp?v=1732343535","url":"https:\/\/booksby.splitshops.com\/products\/learning-with-kernels-support-vector-machines-regularization-optimization-and-beyond-paperback","provider":"Books by splitShops","version":"1.0","type":"link"}