Carleton Author

Musicant, David R.

Department

Computer Science

Journal Title

IEEE Transactions on Neural Networks

Publication Date

2004

Month/Season

March

First Page

268

Publisher

IEEE

Last Page

275

File Name

008_Musicant-Dave_ActiveSetSupportVectorRegression.pdf

Keywords

regression, active set, support vector

Abstract

We present ASVR, a new active set strategy to solve a straightforward reformulation of the standard support vector regression problem. This new algorithm is based on the successful ASVM algorithm for classification problems, and consists of solving a finite number of linear equations with a typically large dimensionality equal to the number of points to be approximated. However, by making use of the Sherman-Morrison-Woodbury formula, a much smaller matrix of the order of the original input space is inverted at each step. The algorithm requires no specialized quadratic or linear programming code, but merely a linear equation solver which is publicly available. ASVR is extremely fast, produces comparable generalization error to other popular algorithms, and is available on the web for download.

Rights Management

Carleton College does not own the copyright to this work and the work is available through the Carleton College Library following the original publisher's policies regarding self-archiving. For more information on the copyright status of this work, refer to the current copyright holder.

RoMEO Color

Green

Preprint Archiving

Yes

Postprint Archiving

Yes

Publisher PDF Archiving

Yes

Contributing Organization

Carleton College

Type

Article

Format

application/pdf

Language

English

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