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Abstract:
We propose a framework based on a parametric quadratic
programming (QP) technique to solve the support vector machine
(SVM) training problem. This framework can be specialized to
obtain two SVM optimization methods. The first solves the fixed
bias problem, while the second starts with an optimal solution
for a fixed bias problem and adjusts the bias until the optimal
value is found. The later method can be applied in conjunction
with any other existing technique which obtains a fixed bias
solution. Moreover, the second method can also be used
independently to solve the complete SVM training problem. A
combination of these two methods is more flexible than each
individual method and, among other things, produces an
incremental algorithm which exactly solves the
1-Norm Soft Margin
SVM optimization problem. Applying
Selective Sampling
techniques may further boost convergence.
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