DocumentCode
2606770
Title
Adaptive classification
Author
Feldkamp, Lee A. ; Feldkamp, Timothy M. ; Prokhorov, Danil V.
Author_Institution
Res. Lab., Ford Motor Co., Dearborn, MI, USA
fYear
2000
fDate
2000
Firstpage
52
Lastpage
57
Abstract
We present an online learning system that is capable of analyzing an input-output data sequence to construct a sequence of binary classifications, without being provided correct class information as part of the training process. The system employs a combination of supervised and unsupervised learning techniques to form two or more behavior models. By examining these models for consistency with the sequence of observed data, an estimate of the class at each time step can be constructed
Keywords
learning systems; neural nets; pattern classification; unsupervised learning; adaptive classification; behavior models; binary classifications; input-output data sequence; online learning system; supervised learning techniques; unsupervised learning techniques; Current measurement; Data analysis; Fault diagnosis; Information analysis; Laboratories; Learning systems; Neural networks; Noise measurement; Performance evaluation; Time measurement;
fLanguage
English
Publisher
ieee
Conference_Titel
Adaptive Systems for Signal Processing, Communications, and Control Symposium 2000. AS-SPCC. The IEEE 2000
Conference_Location
Lake Louise, Alta.
Print_ISBN
0-7803-5800-7
Type
conf
DOI
10.1109/ASSPCC.2000.882446
Filename
882446
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