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Learning with Kernels

Support Vector Machines, Regularization, Optimization, and Beyond
Online Resource
648 Seiten
2014
MIT Press (Hersteller)
9780262256933 (ISBN)
CHF 159,95 inkl. MwSt
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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.

Bernhard Scholkopf is Professor and Director at the Max Planck Institute for Biological Cybernetics in Tubingen, Germany. He is coauthor of Learning with Kernels (2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all published by the MIT Press. Alexander J. Smola is Senior Principal Researcher and Machine Learning Program Leader at National ICT Australia/Australian National University, Canberra.

Erscheint lt. Verlag 20.6.2019
Reihe/Serie Adaptive Computation and Machine Learning Series
Zusatzinfo 138 illus.
Verlagsort Cambridge, Mass.
Sprache englisch
Maße 203 x 254 mm
Themenwelt Mathematik / Informatik Informatik Datenbanken
Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Mathematik / Informatik Mathematik
ISBN-13 9780262256933 / 9780262256933
Zustand Neuware
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