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Proceedings Paper

An algorithm for generating modular hierarchical neural network classifiers: a step toward larger scale applications
Author(s): Davide Roverso
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Paper Abstract

Many-class learning is the problem of training a classifier to discriminate among a large number of target classes. Together with the problem of dealing with high-dimensional patterns (i.e. a high-dimensional input space), the many class problem (i.e. a high-dimensional output space) is a major obstacle to be faced when scaling-up classifier systems and algorithms from small pilot applications to large full-scale applications. The Autonomous Recursive Task Decomposition (ARTD) algorithm is here proposed as a solution to the problem of many-class learning. Example applications of ARTD to neural classifier training are also presented. In these examples, improvements in training time are shown to range from 4-fold to more than 30-fold in pattern classification tasks of both static and dynamic character.

Paper Details

Date Published: 4 August 2003
PDF: 11 pages
Proc. SPIE 5103, Intelligent Computing: Theory and Applications, (4 August 2003); doi: 10.1117/12.490687
Show Author Affiliations
Davide Roverso, Institute for Energy Technology (Norway)

Published in SPIE Proceedings Vol. 5103:
Intelligent Computing: Theory and Applications
Kevin L. Priddy; Peter J. Angeline, Editor(s)

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