Title :
An online learning vector quantization algorithm
Author :
Bharitkar, Sunil ; Filev, Dimitar
Author_Institution :
Signal & Image Process. Inst., Univ. of Southern California, Los Angeles, CA, USA
Abstract :
We propose an online learning algorithm for the learning vector quantization (LVQ) approach in nonlinear supervised classification. The advantage of this approach is the ability of the LVQ to adjust its codebook vectors as new patterns become available, so as to accurately model the class representation of the patterns. Moreover this algorithm does not significantly increase the computational complexity over the original LVQ algorithm
Keywords :
computational complexity; learning (artificial intelligence); pattern classification; vector quantisation; LVQ algorithm; codebook vectors; computational complexity; learning vector quantization; nonlinear supervised classification; online learning algorithm; pattern classification; pattern representation; Computational complexity; Finance; Image processing; Integrated circuit modeling; Learning systems; Milling machines; Pattern recognition; Signal processing; Signal processing algorithms; Vector quantization;
Conference_Titel :
Signal Processing and its Applications, Sixth International, Symposium on. 2001
Conference_Location :
Kuala Lumpur
Print_ISBN :
0-7803-6703-0
DOI :
10.1109/ISSPA.2001.950163
