DocumentCode
2172822
Title
A weighted minimum distance classifier for pattern recognition
Author
Lin, H. ; Venetsanopoulos, A.N.
Author_Institution
Dept. of Electr. Eng., Toronto Univ., Ont., Canada
fYear
1993
fDate
14-17 Sep 1993
Firstpage
904
Abstract
This paper presents a new weighted minimum distance classifier which uses the discriminately power and variance of features. The weights increase the interclass separability while they decrease the intraclass dissimilarity. Two examples are given to show the effectiveness of the method
Keywords
matrix algebra; pattern recognition; interclass separability; intraclass dissimilarity; pattern recognition; weighted minimum distance classifier; Cities and towns; Density functional theory; Density measurement; Equations; Euclidean distance; Multi-layer neural network; Neural networks; Pattern recognition; Power engineering and energy;
fLanguage
English
Publisher
ieee
Conference_Titel
Electrical and Computer Engineering, 1993. Canadian Conference on
Conference_Location
Vancouver, BC
Print_ISBN
0-7803-2416-1
Type
conf
DOI
10.1109/CCECE.1993.332440
Filename
332440
Link To Document