DocumentCode :
1908694
Title :
Quantized, piecewise linear filter network
Author :
Sørensen, John Aasted
Author_Institution :
Tech. Univ. of Denmark, Lyngby, Denmark
fYear :
1993
fDate :
6-9 Sep 1993
Firstpage :
470
Lastpage :
474
Abstract :
A quantization based piecewise linear filter network is defined. A method for the training of this network based on local approximation in the input space is devised. The training is carried out by repeatedly alternating between vector quantization of the training set into quantization classes and equalization of the quantization classes linear filter mean square training errors. The equalization of the mean square training errors is carried out by adapting the boundaries between neighbor quantization classes such that the differences in mean square training errors are reduced
Keywords :
approximation theory; filtering theory; learning (artificial intelligence); neural nets; vector quantisation; input space; linear filter mean square training errors; local approximation; quantization based piecewise linear filter network; quantization classes; vector quantization; Error correction; Mean square error methods; Nonlinear filters; Piecewise linear approximation; Piecewise linear techniques; Tree data structures; Vector quantization; Wiener filter;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks for Processing [1993] III. Proceedings of the 1993 IEEE-SP Workshop
Conference_Location :
Linthicum Heights, MD
Print_ISBN :
0-7803-0928-6
Type :
conf
DOI :
10.1109/NNSP.1993.471841
Filename :
471841
Link To Document :
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