A Comparison of Dynamic Reposing and Tangent Distance for Drug Activity Prediction
Abstract
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. This paper compares dynamic reposing to the tangent distance method on the task of predicting the bio(cid:173) logical activity of musk compounds. In a 20-fold cross-validation,
Cite
Text
Dietterich et al. "A Comparison of Dynamic Reposing and Tangent Distance for Drug Activity Prediction." Neural Information Processing Systems, 1993.Markdown
[Dietterich et al. "A Comparison of Dynamic Reposing and Tangent Distance for Drug Activity Prediction." Neural Information Processing Systems, 1993.](https://mlanthology.org/neurips/1993/dietterich1993neurips-comparison/)BibTeX
@inproceedings{dietterich1993neurips-comparison,
title = {{A Comparison of Dynamic Reposing and Tangent Distance for Drug Activity Prediction}},
author = {Dietterich, Thomas G. and Jain, Ajay N. and Lathrop, Richard H. and Lozano-Pérez, Tomás},
booktitle = {Neural Information Processing Systems},
year = {1993},
pages = {216-223},
url = {https://mlanthology.org/neurips/1993/dietterich1993neurips-comparison/}
}