DocumentCode :
960629
Title :
Active set support vector regression
Author :
Musicant, David R. ; Feinberg, Alexander
Author_Institution :
Dept. of Math. & Comput. Sci., Carleton Coll., Northfield, MN, USA
Volume :
15
Issue :
2
fYear :
2004
fDate :
3/1/2004 12:00:00 AM
Firstpage :
268
Lastpage :
275
Abstract :
This paper presents active set support vector regression (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.
Keywords :
linear programming; matrix algebra; quadratic programming; regression analysis; set theory; Sherman-Morrison-Woodbury formula; active set strategy; active set support vector regression; linear equations; linear programming code; quadratic programming code; Biological information theory; Biological system modeling; Classification algorithms; Context modeling; Data analysis; Diseases; Equations; Linear programming; Testing; Vectors; Algorithms; Regression Analysis;
fLanguage :
English
Journal_Title :
Neural Networks, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/TNN.2004.824259
Filename :
1288231
Link To Document :
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