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Title: Support Vector Machine algorithm for regression and classification

Abstract

The software is an implementation of the Support Vector Machine (SVM) algorithm that was invented and developed by Vladimir Vapnik and his co-workers at AT&T Bell Laboratories. The specific implementation reported here is an Active Set method for solving a quadratic optimization problem that forms the major part of any SVM program. The implementation is tuned to specific constraints generated in the SVM learning. Thus, it is more efficient than general-purpose quadratic optimization programs. A decomposition method has been implemented in the software that enables processing large data sets. The size of the learning data is virtually unlimited by the capacity of the computer physical memory. The software is flexible and extensible. Two upper bounds are implemented to regulate the SVM learning for classification, which allow users to adjust the false positive and false negative rates. The software can be used either as a standalone, general-purpose SVM regression or classification program, or be embedded into a larger software system.

Authors:
;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1230694
Report Number(s):
ActiveSVM; 001690MLTPL00
DOE Contract Number:
W31-109-31eng
Resource Type:
Software
Software Revision:
00
Software Package Number:
001690
Software Package Contents:
Media Directory; Software Abstract; Media includes Source Code / 1 CD-ROM
Software CPU:
MLTPL
Open Source:
No
Source Code Available:
Yes
Country of Publication:
United States

Citation Formats

Yu, Chenggang, and Zavaljevski, Nela. Support Vector Machine algorithm for regression and classification. Computer software. Vers. 00. USDOE. 1 Aug. 2001. Web.
Yu, Chenggang, & Zavaljevski, Nela. (2001, August 1). Support Vector Machine algorithm for regression and classification (Version 00) [Computer software].
Yu, Chenggang, and Zavaljevski, Nela. Support Vector Machine algorithm for regression and classification. Computer software. Version 00. August 1, 2001.
@misc{osti_1230694,
title = {Support Vector Machine algorithm for regression and classification, Version 00},
author = {Yu, Chenggang and Zavaljevski, Nela},
abstractNote = {The software is an implementation of the Support Vector Machine (SVM) algorithm that was invented and developed by Vladimir Vapnik and his co-workers at AT&T Bell Laboratories. The specific implementation reported here is an Active Set method for solving a quadratic optimization problem that forms the major part of any SVM program. The implementation is tuned to specific constraints generated in the SVM learning. Thus, it is more efficient than general-purpose quadratic optimization programs. A decomposition method has been implemented in the software that enables processing large data sets. The size of the learning data is virtually unlimited by the capacity of the computer physical memory. The software is flexible and extensible. Two upper bounds are implemented to regulate the SVM learning for classification, which allow users to adjust the false positive and false negative rates. The software can be used either as a standalone, general-purpose SVM regression or classification program, or be embedded into a larger software system.},
doi = {},
year = {Wed Aug 01 00:00:00 EDT 2001},
month = {Wed Aug 01 00:00:00 EDT 2001},
note =
}

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