Active Learning for Parzen Window Classifier
The problem of active learning is approached in this paper by minimizing directly an estimate of the expected test error. The main difficulty in this “optimal” strategy is that output probabilities need to be estimated accurately. We suggest here different methods for estimating those efficiently. In this context, the Parzen window classifier is considered because it is both simple and probabilistic. The analysis of experimental results highlights that regularization is a key ingredient for this strategy.