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https://www.cs.utah.edu/~piyush/teaching/learning-with-kernels.pdf
Scho¨lkopf and Smola: Learning with Kernels — Confidential draft, please do not circulate — 2001/03/02 20:32 1 A Tutorial Introduction This chapter describes the central ideas of support vector (SV) learning in a nutshell. Its goal is to provide an overview of the basic concepts. One of these concepts is that of a kernel.
https://mitpress.mit.edu/books/learning-kernels
A comprehensive introduction to Support Vector Machines and related kernel methods. 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 ...
https://dl.acm.org/doi/book/10.5555/559923
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.
https://www.bookdepository.com/Learning-with-Kernels-Bernhard-Scholkopf/9780262194754
Dec 15, 2001 · 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.4.1/5(32)
https://cds.cern.ch/record/791819/files/0262194759_TOC.pdf
LearningwithKernels SupportVectorMachines,Regularization,Optimization,andBeyond BernhardScholkopf AlexanderJ. Smola TheMITPress Cambridge,Massachusetts
https://www.booklibrarian.com/pdfepub/learning-with-kernels
Learning with Kernels Book Summary : This volume provides an introduction to SVMs and related kernel methods. It provides concepts necessary to enable a reader to enter the world of machine learning using theoretical kernel algorithms and to understand and apply the algorithms that have been developed over the last few years.
https://books.google.com/books/about/Learning_with_Kernels.html?id=y8ORL3DWt4sC
A comprehensive introduction to Support Vector Machines and related kernel methods. 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.4.5/5(9)
http://scholar.google.com/citations?user=DZ-fHPgAAAAJ&hl=en
MA Hearst, ST Dumais, E Osuna, J Platt, B Scholkopf. IEEE Intelligent Systems and their applications 13 (4), 18-28, 1998. 2642: 1998: Advances in kernel methods support vector learning. B Schèolkopf, CJC Burges, AJ Smola. ... Advances in Kernel Methods: Support Vector Learning 11, 1999. 1572: 1999: Large scale multiple kernel learning.
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