Massive Data Classification via Unconstrained Support Vector Machines
Abstract
A highly accurate algorithm, based on support vector machines
formulated as linear programs [13, 1], is proposed
here as a completely unconstrained minimization problem
[15]. Combined with a chunking procedure [2] this approach,
which requires nothing more complex than a linear equation
solver, leads to a simple and accurate method for classifying
million-point datasets. Because a 1-norm support vector
machine underlies the proposed approach, the method suppresses
input space features as well. A state-of-the-art linear
programming package, CPLEX [10], fails to solve problems
handled by the proposed algorithm.
Subject
linear program
massive data classification
support vector machines
Permanent Link
http://digital.library.wisc.edu/1793/64336Citation
06-01