4 August 2003 An algorithm for generating modular hierarchical neural network classifiers: a step toward larger scale applications
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Proceedings Volume 5103, Intelligent Computing: Theory and Applications; (2003); doi: 10.1117/12.490687
Event: AeroSense 2003, 2003, Orlando, Florida, United States
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.
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Davide Roverso, "An algorithm for generating modular hierarchical neural network classifiers: a step toward larger scale applications", Proc. SPIE 5103, Intelligent Computing: Theory and Applications, (4 August 2003); doi: 10.1117/12.490687; https://doi.org/10.1117/12.490687
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KEYWORDS
Neural networks

Image classification

Detection and tracking algorithms

Evolutionary algorithms

Classification systems

Principal component analysis

Algorithm development

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