Abstract
A novelty detection robust model is studied in the paper. It is based on contaminated robust models which produce a set of probability distributions of data points instead of the empirical distribution. The minimax and minimin strategies are used to construct optimal separating functions. An algorithm for computing the optimal parameters of the novelty detection model is reduced to a finite number of standard SVM tasks with weighted data points. Experimental results with synthetic and some real data illustrate the proposed novelty detection robust model.
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