A Comparison of Dynamic Reposing and Tangent Distance for Drug Activity Prediction

In drug activity prediction (as in handwritten character recogni(cid:173) tion), the features extracted to describe a training example depend on the pose (location, orientation, etc.) of the example. In hand(cid:173) written character recognition, one of the best techniques for ad(cid:173) dressing this problem is the tangent distance method of Simard, LeCun and Denker (1993). Jain, et al. (1993a; 1993b) introduce a new technique-dynamic reposing-that also addresses this prob(cid:173) lem. Dynamic reposing iteratively learns a neural network and then reposes the examples in an effort to maximize the predicted out(cid:173) put values. New models are trained and new poses computed until models and poses converge.