Title :
Designing large scale classifiers
Author :
Porter, William A. ; Liu, Wei
Author_Institution :
Dept. of Electr. & Comput. Eng., Alabama Univ., Huntsville, AL, USA
fDate :
31 Mar-2 Apr 1996
Abstract :
In this study we present a design for hierarchical modular classifiers. The design features an algorithm which selects a set of exemplars. Using these exemplars the classification problem is decomposed into a family of disjoint subproblems. A classification module is trained for each subproblem. The collection of classification modules and a rule book for their use then comprise the resultant design
Keywords :
encoding; learning (artificial intelligence); multilayer perceptrons; pattern classification; set theory; backpropagation; classification module; code book; design features; disjoint subproblems; hierarchical modular classifiers; large scale classifiers; Algorithm design and analysis; Books; Computational efficiency; Concurrent computing; Image recognition; Large-scale systems; Neural networks; Resonance; Robustness; Supervised learning;
Conference_Titel :
System Theory, 1996., Proceedings of the Twenty-Eighth Southeastern Symposium on
Conference_Location :
Baton Rouge, LA
Print_ISBN :
0-8186-7352-4
DOI :
10.1109/SSST.1996.493489
