Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/54452
Type of publication: research article
Type of publication (PDB): Straipsnis Clarivate Analytics Web of Science / Article in Clarivate Analytics Web of Science (S1)
Field of Science: Informatika / Informatics (N009)
Author(s): Raudys, Šarūnas;Tamošiūnaitė, Minija
Title: Biologically inspired architecture of feedforward networks for signal classification
Is part of: Advances in pattern recognition : joint IAPR international workshops SSPR 2000 and SPR 2000 Alicante, Spain, August 30 – September 1, 2000 : proceedings. Berlin, Heidelberg : Springer, 2000
Extent: p. 727-736
Date: 2000
Series/Report no.: (Lecture notes in computer science. Vol. 1876 0302-9743)
Keywords: Signal classification;Neural network;Pattern recognition
ISBN: 9783540679462
Abstract: The hypothesis is that in the lowest bidden layers of biological systems "local subnetworks" are smoothing an input signal. The smoothing accuracy may serve as a feature to feed the subsequent layers of the pattern classification network. The present paper suggests a multistage supervised and "unsupervised" training approach for design and training of multilayer feed-forward networks. Following to the methodology used in the statistical pattern recognition systems we split functionally the decision making process into two stages. In an initial stage, we smooth the input signal in a number of different ways and, in the second stage, we use the smoothing accuracy as anew feature to perform a final classification
Internet: https://doi.org/10.1007/3-540-44522-6_75
Affiliation(s): Taikomosios informatikos katedra
Vytauto Didžiojo universitetas
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications

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