An online learning vector quantization algorithm

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.