Title :
Hybrid learning vector quantization
Author :
Lai, Yuan-Cheng ; Yu, Shiaw-Shian ; Chou, Sheng-Lin
Author_Institution :
Comput. & Commun. Res. Labs., Ind. Technol. Res. Inst., Hsinchu, Taiwan
Abstract :
In this paper, a hybrid learning vector quantization algorithm is proposed. It modifies both the position of representative points and normalization parameters. Some of the experiments are operated on the synthetic and real data. The results show that the proposed hybrid learning vector quantization algorithm is applicable.
Keywords :
learning (artificial intelligence); neural nets; pattern classification; vector quantisation; hybrid learning vector quantization; normalization parameters; representative points; Clustering algorithms; Computer networks; Decision theory; Nearest neighbor searches; Neural networks; Neurons; Pattern classification; Unsupervised learning; Vector quantization; Zinc;
Conference_Titel :
Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on
Print_ISBN :
0-7803-1421-2
DOI :
10.1109/IJCNN.1993.714253
