Generative versus Discriminative Training of RBMs for Classification of fMRI Images

by Tanya Schmah, Geoffrey E. Hinton, Richard S. Zemel, Steven L. Small, Stephen Strother

This is much simpler than the previous RBM. It simply train one RBM for each class. Then they use Bayes theorem to calculate the posterior probability to classify (with the assumption that the prior is equal of each class). The only difficult part is the normalization factors. They use a discriminative way of learning for this ratio. In the discriminative learning, the fraction is something like a logistic function and the partial derivatives can be calculated very easily.

They carry out the experiments on fMRI data, on which logistict regression with regularization does not work well.