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Abstract:
When constructing a classifier, the probability of correct
classification of future data points should be maximized. In the
current paper this desideratum is translated in a very direct way
into an optimization problem, which is solved using methods from
convex optimization. We also show how to exploit Mercer kernels
in this setting to obtain nonlinear decision boundaries. A
worst-case bound on the probability of misclassification of
future data is obtained explicitly.
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