Active Support Vector Machine Classification
Abstract
An active set strategy is applied to the dual of a simple reformula(cid:173) tion of the standard quadratic program of a linear support vector machine. This application generates a fast new dual algorithm that consists of solving a finite number of linear equations, with a typically large dimensionality equal to the number of points to be classified. However, by making novel use of the Sherman-Morrison(cid:173) Woodbury formula, a much smaller matrix of the order of the orig(cid:173) inal input space is inverted at each step. Thus, a problem with a 32-dimensional input space and 7 million points required inverting positive definite symmetric matrices of size 33 x 33 with a total run(cid:173) ning time of 96 minutes on a 400 MHz Pentium II. The algorithm requires no specialized quadratic or linear programming code, but merely a linear equation solver which is publicly available.
Cite
Text
Mangasarian and Musicant. "Active Support Vector Machine Classification." Neural Information Processing Systems, 2000.Markdown
[Mangasarian and Musicant. "Active Support Vector Machine Classification." Neural Information Processing Systems, 2000.](https://mlanthology.org/neurips/2000/mangasarian2000neurips-active/)BibTeX
@inproceedings{mangasarian2000neurips-active,
title = {{Active Support Vector Machine Classification}},
author = {Mangasarian, Olvi L. and Musicant, David R.},
booktitle = {Neural Information Processing Systems},
year = {2000},
pages = {577-583},
url = {https://mlanthology.org/neurips/2000/mangasarian2000neurips-active/}
}