OBJECTIVE A self-organizing map (SOM) is a competitive artificial neural network with unsupervised learning. To increase the SOM learning effect, a fuzzy-soft learning vector quantization (FSLVQ) algorithm has been proposed in the literature, using fuzzy functions to approximate lateral neural interaction of the SOM. However, the computational performance of FSLVQ is still not good enough, espe...

We propose a new learning method, "Generalized Learning Vector Quantization (GLVQ)," in which reference vectors are updated based on the steepest descent method in order to minimize the cost function . The cost function is determined so that the obtained learning rule satisfies the convergence condition. We prove that Kohonen's rule as used in LVQ does not satisfy the convergence condition and ...

We compare the performance of ve algorithms for vector quan-tisation and clustering analysis: the Self-Organising Map (SOM) and Learning Vector Quantization (LVQ) algorithms of Kohonen, the Linde-Buzo-Gray (LBG) algorithm, the MultiLayer Perceptron (MLP) and the GMM/EM algorithm for Gaussian Mixture Models (GMM). We propose that the GMM/EM provides a better representation of the speech space an...

Recent years have witnessed the increase in demand for fast, efficient, and high quality multimedia communication applications over the wired and wireless networks. The constraint in bandwidth has paved the way for researchers to embark their efforts in finding efficient compression techniques for image and video. The demand for high quality of data communication has open up the research area i...

Unsupervised self-organizing maps (SOMs), as well as supervised learning by Learning Vector Quantization (LVQ) can be defined for string variables, too. Their computing becomes possible when the SOM and the LVQ algorithms are expressed as batch versions, and when the average over a list of symbol strings is defined to be the string that has the smallest sum of generalized distance functions fro...