DocumentCode
3442092
Title
A class of order statistics learning vector quantizers
Author
Pitas, I. ; Kotropoulos, C. ; Nikolaidis, N. ; Yang, R. ; Gabbouj, M.
Author_Institution
Dept. of Electr. & Comput. Eng., Thessaloniki Univ., Greece
Volume
6
fYear
1994
fDate
30 May-2 Jun 1994
Firstpage
387
Abstract
A novel class of Learning Vector Quantizers (LVQs) based on multivariate order statistics is proposed in order to overcome the drawback that the estimators for obtaining the reference vectors in LVQ do not have robustness either against erroneous choices for the winner vector or against the outliers that may exist in vector-valued observations. The performance of the proposed variants of LVQ is demonstrated by experiments. In the case of marginal median LVQ, its asymptotic properties are derived as well
Keywords
learning (artificial intelligence); neural nets; statistics; vector quantisation; asymptotic properties; learning vector quantizers; marginal median LVQ; multivariate order statistics; Artificial neural networks; Error correction codes; Laboratories; Network topology; Neural networks; Robustness; Signal processing; Signal processing algorithms; Statistics; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 1994. ISCAS '94., 1994 IEEE International Symposium on
Conference_Location
London
Print_ISBN
0-7803-1915-X
Type
conf
DOI
10.1109/ISCAS.1994.409607
Filename
409607
Link To Document