Robust linear and support vector regression
Mangasarian, O.L.
Musicant, D.R.
Dept. of Comput. Sci., Wisconsin Univ., Madison, WI;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Sep 2000
Volume: 22,
Issue: 9
On page(s): 950-955
ISSN: 0162-8828
References Cited: 35
CODEN: ITPIDJ
INSPEC Accession Number: 6744984
Digital Object Identifier: 10.1109/34.877518
Current Version Published: 2002-08-06
Abstract
The robust Huber M-estimator, a differentiable cost function that
is quadratic for small errors and linear otherwise, is modeled exactly,
in the original primal space of the problem, by an easily solvable
simple convex quadratic program for both linear and nonlinear support
vector estimators. Previous models were significantly more complex or
formulated in the dual space and most involved specialized numerical
algorithms for solving the robust Huber linear estimator. Numerical test
comparisons with these algorithms indicate the computational
effectiveness of the new quadratic programming model for both linear and
nonlinear support vector problems. Results are shown on problems with as
many as 20000 data points, with considerably faster running times on
larger problems
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