Classification of Incomplete Data Using the Fuzzy ARTMAP Neural Network
Date
2000-01
DOI
Authors
Granger, Eric
Rubin, Mark
Grossberg, Stephen
Lavoie, Pierre
Version
OA Version
Citation
Abstract
The fuzzy ARTMAP neural network is used to classify data that is incomplete in one or more ways. These include a limited number of training cases, missing components, missing class labels, and missing classes. Modifications for dealing with such incomplete data are introduced, and performance is assessed on an emitter identification task using a data base of radar pulses
Description
License
Copyright 2000 Boston University. Permission to copy without fee all or part of this material is granted provided that: 1. The copies are not made or distributed for direct commercial advantage; 2. the report title, author, document number, and release date appear, and notice is given that copying is by permission of BOSTON UNIVERSITY TRUSTEES. To copy otherwise, or to republish, requires a fee and / or special permission.