Massive Support Vector Regression (1999)
| Venue: | Data Mining Institute, Computer Sciences Department, University of Wisconsin |
| Citations: | 8 - 4 self |
BibTeX
@TECHREPORT{Mangasarian99massivesupport,
author = {O. L. Mangasarian and David R. Musicant},
title = {Massive Support Vector Regression},
institution = {Data Mining Institute, Computer Sciences Department, University of Wisconsin},
year = {1999}
}
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Abstract
The problem of tolerant data fitting by a nonlinear surface, induced by a kernel-based support vector machine [19], is formulated as a linear program with fewer number of variables than that of other linear programming formulations [17]. A generalization of the linear programming chunking algorithm [1] for arbitrary kernels [10] is implemented for solving problems with very large datasets wherein chunking is performed on both data points and problem variables. The proposed approach tolerates a small error, which is adjusted parametrically, while fitting the given data. This leads to improved fitting of noisy data as demonstrated computationally. Comparative numerical results indicate an average time reduction as high as 26.0%, with a maximal time reduction of 79.7%. Additionally, linear programs with as many as 16,000 data points and more than a billion nonzero matrix elements are solved. 1 Introduction Tolerating a small error in fitting a given set of data, i.e. disregarding errors ...
