Bayesian transfer learning for noisy channels
Nathan H. Parrish, Maya R. Gupta · 2011
We consider the problem of classifying a signal that is the output of a linear, time-invariant channel in the presence of additive noise, given two distinct sets of labeled data: one dataset of examples of the signals input to the channel, and a second dataset of example signals corrupted by the channel. We propose a distribution-based Bayesian quadratic discriminant analysis classifier that uses the input examples along with a model for the channel to form a prior for the likelihood of the output examples. Preliminary experiments with this proposed transfer BDA classifier show that it effectively uses both sets of data and is also robust to errors in channel modeling.