creators_name: Intrator, Nathan
creators_name: Edelman, Shimon
type: preprint
datestamp: 1997-11-17
lastmod: 2011-03-11 08:54:05
metadata_visibility: show
title: How to Make a Low-Dimensional Representation Suitable for Diverse Tasks
subjects: cog-psy
full_text_status: public
abstract: 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.
date: 1996
date_type: published
refereed: FALSE
citation:   Intrator, Nathan and Edelman, Shimon  (1996) How to Make a Low-Dimensional Representation Suitable for Diverse Tasks.  [Preprint]     
document_url: http://cogprints.org/571/2/199711005.ps