ML Anthology

Using Analytic QP and Sparseness to Speed Training of Support Vector Machines

John C. Platt
NeurIPS 1998 pp. 557-563
/neurips/1998/platt1998neurips-using/

Abstract

Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming (QP) problem. This paper proposes an al(cid:173) gorithm for training SVMs: Sequential Minimal Optimization, or SMO. SMO breaks the large QP problem into a series of smallest possible QP problems which are analytically solvable. Thus, SMO does not require a numerical QP library. SMO's computation time is dominated by eval(cid:173) uation of the kernel, hence kernel optimizations substantially quicken SMO. For the MNIST database, SMO is 1.7 times as fast as PCG chunk(cid:173) ing; while for the UCI Adult database and linear SVMs, SMO can be 1500 times faster than the PCG chunking algorithm.

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Cite

Text

Platt. "Using Analytic QP and Sparseness to Speed Training of Support Vector Machines." Neural Information Processing Systems, 1998.

Markdown

[Platt. "Using Analytic QP and Sparseness to Speed Training of Support Vector Machines." Neural Information Processing Systems, 1998.](https://mlanthology.org/neurips/1998/platt1998neurips-using/)

BibTeX

@inproceedings{platt1998neurips-using,
  title     = {{Using Analytic QP and Sparseness to Speed Training of Support Vector Machines}},
  author    = {Platt, John C.},
  booktitle = {Neural Information Processing Systems},
  year      = {1998},
  pages     = {557-563},
  url       = {https://mlanthology.org/neurips/1998/platt1998neurips-using/}
}