Support Vector Machines in R
TLDR
The purpose of this paper is to present and compare these implementations of support vector machines, among the most popular and efficient classification and regression methods currently available.Abstract:
Being among the most popular and efficient classification and regression methods
currently available, implementations of support vector machines exist in almost every
popular programming language. Currently four R packages contain SVM related software.
The purpose of this paper is to present and compare these implementations. (authors' abstract)read more
Citations
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References
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Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Proceedings ArticleDOI
Advances in kernel methods: support vector learning
TL;DR: Support vector machines for dynamic reconstruction of a chaotic system, Klaus-Robert Muller et al pairwise classification and support vector machines, Ulrich Kressel.
Fast training of support vector machines using sequential minimal optimization, advances in kernel methods
TL;DR: SMO breaks this large quadratic programming problem into a series of smallest possible QP problems, which avoids using a time-consuming numerical QP optimization as an inner loop and hence SMO is fastest for linear SVMs and sparse data sets.