%A Nathan Intrator
%A Shimon Edelman
%T How to Make a Low-Dimensional Representation Suitable for Diverse Tasks
%X 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.
%D 1996
%L cogprints571

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