DocumentCode
3410049
Title
Designing large scale classifiers
Author
Porter, William A. ; Liu, Wei
Author_Institution
Dept. of Electr. & Comput. Eng., Alabama Univ., Huntsville, AL, USA
fYear
1996
fDate
31 Mar-2 Apr 1996
Firstpage
153
Lastpage
157
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;
fLanguage
English
Publisher
ieee
Conference_Titel
System Theory, 1996., Proceedings of the Twenty-Eighth Southeastern Symposium on
Conference_Location
Baton Rouge, LA
ISSN
0094-2898
Print_ISBN
0-8186-7352-4
Type
conf
DOI
10.1109/SSST.1996.493489
Filename
493489
Link To Document