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https://www.amazon.com/Learning-Kernels-Regularization-Optimization-Computation/dp/0262194759
Mar 02, 2018 · Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning) [Bernhard Schlkopf, Alexander J. Smola] on Amazon.com. *FREE* shipping on qualifying offers. A comprehensive introduction to Support Vector Machines and related kernel methods. In the 1990sCited by: 16910
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://people.eecs.berkeley.edu/~malik/cs294/decoste-scholkopf.pdf
TRAINING INVARIANT SUPPORT VECTOR MACHINES 165 and the increase in training set size. If the size of a training set is multiplied by a number of desired invariances (by generating a corresponding number of artificial examples for each training pattern), the resulting training sets can get rather large (as the ones used by
https://www.microsoft.com/en-us/research/publication/fast-training-of-support-vector-machines-using-sequential-minimal-optimization/
This chapter describes a new algorithm for training Support Vector Machines: Sequential Minimal Optimization, or SMO. Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming (QP) optimization problem. SMO breaks this QP problem into a …Cited by: 7758
https://www.scirp.org/journal/PaperInformation.aspx?paperID=27408
Support Vector-based learning methods are an important part of Computational Intelligence techniques. Recent efforts have been dealing with the problem of learning from very large datasets. This paper reviews the most commonly used formulations of support vector machines for regression (SVRs) aiming to emphasize its usability on large-scale applications.Cited by: 12
http://www.stat.purdue.edu/~yuzhu/stat598m3/Papers/NewSVM.pdf
New Support Vector Algorithms Bernhard Scholkopf¨ ¤ Alex J. Smola GMD FIRST, 12489 Berlin, Germany, and Department of Engineering, Australian National University, Canberra 0200, Australia Robert C. Williamson DepartmentofEngineering,AustralianNationalUniversity,Canberra …
https://alex.smola.org/papers/2004/SmoSch04.pdf
A tutorial on support vector regression∗ ALEX J. SMOLA and BERNHARD SCHOLKOPF¨ ... Keywords: machine learning, support vector machines, regression estimation 1. Introduction The purpose of this paper is twofold. It should serve as a self-contained introduction to Support Vector regression for …
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. ... Support Vector Learning 11, 1999. 1572: 1999: Large scale multiple kernel learning.
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://link.springer.com/chapter/10.1007/0-387-25465-X_12
Support Vector Machines (SVMs) are a set of related methods for supervised learning, applicable to both classification and regression problems. A SVM classifiers creates a maximum-margin hyperplane that lies in a transformed input space and splits the example classes, while maximizing the distance to the nearest cleanly split examples.
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