Robust classification of signal estimates given a channel model

In many signal processing applications, a signal to be classified has been corrupted by a channel and additive noise. A standard approach is to estimate the clean signal, then classify it. We consider two robust approaches that account for the estimation procedure. The first approach is an application of the MAP rule for noisy features, and the second is an approach for discriminative classifiers that treats that training points as random. An experiment confirms that the robust approaches offer performance gains.