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Learning with Kernels

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Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Schölkopf, B. (2002). Learning with Kernels. Talk presented at 13th European Conference on Machine Learning and the 6th European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2002). Helsinki, Finland. 2002-08-19 - 2002-08-23.


Cite as: http://hdl.handle.net/21.11116/0000-0007-8DC2-F
Abstract
In the 90s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to the development of a new class of theoretically elegant learning machines which use a central concept of SVMs -- kernels -- for a number of different learning tasks. Kernel machines now provide a modular and simple to use framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm, and they have been shown to perform very well in problems ranging from computer vision to text categorization and applications in computational biology.