article · 2007
Parallel support vector machine training
Kristian Woodsend, Jacek Gondzio
Cited by 0Abstract and details only
Abstract
Support Vector Machines (SVMs) are powerful machine learning techniques for classification and regression, and they offer state-of-the-art performance. The training of an SVM is computationally expensive and relies on optimization. The core of the approach is a dense convex quadratic optimization problem (QP), min z 1 2 zT Y KY z − e T z s.t. y T z = 0 (1) 0 ≤ z ≤ τe, where K ∈ R n×n is the matrix resulting from applying a (possibly nonlinear) kernel function to all points of the data set, y ∈ {−1, 1} n the vector of classification labels, Y = diag(y), z ∈ R n the support vector variables, τ> 0 a scalar parameter of the problem, and e ∈ R n the vector of all ones. Due to the dense Hessian matrix, a general-purpose QP solver needs O(n 3) flops to solve such a problem. This complexity result makes applying SVMs to large-scale data sets challenging, and
Cite this paper
Woodsend, K., & Gondzio, J. (2007). Parallel support vector machine training.
Woodsend, Kristian, and Jacek Gondzio. “Parallel support vector machine training.” 2007.
Woodsend, Kristian, and Jacek Gondzio. 2007. “Parallel support vector machine training.”
Woodsend, K. and Gondzio, J. (2007) ‘Parallel support vector machine training’.
@article{woodsend2007parallel,
title = {{Parallel support vector machine training}},
author = {Woodsend, Kristian and Gondzio, Jacek},
year = {2007}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.