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
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;
Conference_Titel :
Neural Networks,1997., International Conference on
Conference_Location :
Houston, TX
Print_ISBN :
0-7803-4122-8
DOI :
10.1109/ICNN.1997.616186
