Document Detail


MART: a multichannel ART-based neural network.
MedLine Citation:
PMID:  18252435     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
This paper describes MART, an ART-based neural network for adaptive classification of multichannel signal patterns without prior supervised learning. Like other ART-based classifiers, MART is especially suitable for situations in which not even the number of pattern categories to be distinguished is known a priori; its novelty lies in its truly multichannel orientation, especially its ability to quantify and take into account during pattern classification the different changing reliability of the individual signal channels. The extent to which this ability can reduce the creation of spurious or duplicate categories (a major problem for ART-based classifiers of noisy signals) is illustrated by evaluation of its performance in classifying QRS complexes in two-channel ECG traces which were taken from the MIT-BIH database and contaminated with noise.
Authors:
M Fernandez-Delgado; S Barro Ameneiro
Related Documents :
24638745 - Mathematical probability model for obstructive sleep apnea syndrome (osas).
21295595 - A powerful truncated tail strength method for testing multiple null hypotheses in one d...
Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council     Volume:  9     ISSN:  1045-9227     ISO Abbreviation:  -     Publication Date:  1998  
Date Detail:
Created Date:  2008-02-06     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101211035     Medline TA:  IEEE Trans Neural Netw     Country:  United States    
Other Details:
Languages:  eng     Pagination:  139-50     Citation Subset:  -    
Affiliation:
Dept. of Electron. and Comput., Santiago de Compostela Univ.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


Previous Document:  Adaptive unsupervised extraction of one component of a linear mixture with a single neuron.
Next Document:  Learning in certainty-factor-based multilayer neural networks for classification.