DocumentCode
315250
Title
A vector quantisation reduction method for the probabilistic neural network
Author
Zaknich, Anthony
Author_Institution
Dept. of Electr. & Electron. Eng., Western Australia Univ., Nedlands, WA, Australia
Volume
2
fYear
1997
fDate
9-12 Jun 1997
Firstpage
1117
Abstract
This paper introduces a vector quantisation method to reduce the probabilistic neural network classifier size. It has been derived from the modified probabilistic neural network which was developed as a general regression technique but can also be used for classification. It is a very practical and easy to implement method requiring a very low level of computation. The method is described and demonstrated using 4 different sets of classification data
Keywords
decision theory; feedforward neural nets; pattern classification; probability; vector quantisation; classifier size; probabilistic neural network; vector quantisation reduction method; Associate members; Equations; Feedforward systems; Information processing; Intelligent networks; Intelligent systems; Neural networks; Smoothing methods; Testing; Vector quantization;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks,1997., International Conference on
Conference_Location
Houston, TX
Print_ISBN
0-7803-4122-8
Type
conf
DOI
10.1109/ICNN.1997.616186
Filename
616186
Link To Document