Category Similarity as a Predictor for SVM Learning Performance

Fabio Pardi

In this paper we propose a method for the prediction of learning performance in Support Vector Machines based on a novel definition of intra- and inter-class similarity. Our measure of category similarity can be easily estimated from the learning data. In the second part of the paper we provide experimental evidence to support the effectiveness of this measure.


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