TY  - GEN
ID  - cogprints571
UR  - http://cogprints.org/571/
A1  - Intrator, Nathan
A1  - Edelman, Shimon
TI  - How to Make a Low-Dimensional Representation Suitable for Diverse Tasks
Y1  - 1996///
N2  - We consider training classifiers for multiple tasks as a method for improving generalization and obtaining a better low-dimensional representation. To that end, we introduce a hybrid training methodology for MLP networks; the utility of the hidden-unit representation is assessed by embedding it into a 2D space using multidimensional scaling. The proposed methodology is tested on a highly nonlinear image classification task.
AV  - public
ER  -

<script src='https://archive-bar.soton.ac.uk/archive-bar.js'></script>
<script src='https://archive-bar.soton.ac.uk/google-analytics.js'></script>