An improved training algorithm for support vector machines
Osuna, E.
Freund, R.
Girosi, F.
CBCL, MIT, Cambridge, MA;
This paper appears in: Neural Networks for Signal Processing [1997] VII. Proceedings of the 1997 IEEE Workshop
Publication Date: 24-26 Sep 1997
On page(s): 276-285
Meeting Date: 09/24/1997 - 09/26/1997
Location: Amelia Island, FL, USA
ISBN: 0-7803-4256-9
References Cited: 8
INSPEC Accession Number: 5739855
Digital Object Identifier: 10.1109/NNSP.1997.622408
Current Version Published: 2002-08-06
Abstract
We investigate the problem of training a support vector machine
(SVM) on a very large database in the case in which the number of
support vectors is also very large. Training a SVM is equivalent to
solving a linearly constrained quadratic programming (QP) problem in a
number of variables equal to the number of data points. This
optimization problem is known to be challenging when the number of data
points exceeds few thousands. In previous work done by us as well as by
other researchers, the strategy used to solve the large scale QP problem
takes advantage of the fact that the expected number of support vectors
is small (<3,000). Therefore, the existing algorithms cannot deal
with more than a few thousand support vectors. In this paper we present
a decomposition algorithm that is guaranteed to solve the QP problem and
that does not make assumptions on the expected number of support
vectors. In order to present the feasibility of our approach we consider
a foreign exchange rate time series database with 110,000 data points
that generates 100,000 support vectors
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