The problem of extracting a minimal number of data points
from a large dataset, in order to generate a support vector
machine (SVM) classi er, is formulated as a concave minimization
problem and solved by a nite number of linear
programs. This minimal set of data points, which is the
smallest number of support vectors that completely characterize
a separating plane classi er, is considerably smaller
than that required by a standard 1-norm support vector machine
with or without feature selection. The proposed approach
also incorporates a feature selection procedure that
results in a minimal number of input features used by the
classi er. Tenfold cross validation gives as good or better
test results using the proposed minimal support vector machine
(MSVM) classi er based on the smaller set of data
points compared to a standard 1-norm support vector machine
classi er. The reduction in data points used by an
MSVM classi er over those used by a 1-norm SVM classi er
averaged 66% on seven public datasets and was as high as
81%. This makes MSVM a useful incremental classi cation
tool which maintains only a small fraction of a large dataset
before merging and processing it with new incoming data
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