Title :
Building the genetic learning rule for adaptive vector quantization in neural networks
Author :
Lee, C.H. ; See, S.K.E.
Author_Institution :
Dept. of Comput. Sci., City Polytech. of Hong Kong, Kowloon, Hong Kong
Abstract :
We present a new unsupervised learning algorithm by means of incorporating the genetic algorithm idea into the neural networks for adaptive vector quantisation. From the simulations, the model can cluster the noisy data and recall the patterns accurately
Keywords :
genetic algorithms; neural nets; unsupervised learning; vector quantisation; adaptive vector quantization; genetic algorithm; genetic learning rule; neural networks; noisy data clustering; patterns; simulations; unsupervised learning algorithm; Adaptive systems; Biological system modeling; Cities and towns; Clustering algorithms; Computational modeling; Computer science; Genetic algorithms; Intelligent networks; Neural networks; Vector quantization;
Conference_Titel :
TENCON '94. IEEE Region 10's Ninth Annual International Conference. Theme: Frontiers of Computer Technology. Proceedings of 1994
Print_ISBN :
0-7803-1862-5
DOI :
10.1109/TENCON.1994.369199
