Distributed unsupervised learning using the multisoft machine
Information Sciences—Informatics and Computer Science: An International Journal
Novel vector quantiser design using reinforced learning as a pre-process
Signal Processing
A SOM based 2500 – isolated – farsi – word speech recognizer
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Reformulating Learning Vector Quantization and Radial Basis Neural Networks
Fundamenta Informaticae
Wavelet fuzzy LVQ based speaker verification system
International Journal of Speech Technology
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This paper presents the development of fuzzy algorithms for learning vector quantization (FALVQ). These algorithms are derived by minimizing the weighted sum of the squared Euclidean distances between an input vector, which represents a feature vector, and the weight vectors of a competitive learning vector quantization (LVQ) network, which represent the prototypes. This formulation leads to competitive algorithms, which allow each input vector to attract all prototypes. The strength of attraction between each input and the prototypes is determined by a set of membership functions, which can be selected on the basis of specific criteria. A gradient-descent-based learning rule is derived for a general class of admissible membership functions which satisfy certain properties. The FALVQ 1, FALVQ 2, and FALVQ 3 families of algorithms are developed by selecting admissible membership functions with different properties. The proposed algorithms are tested and evaluated using the IRIS data set. The efficiency of the proposed algorithms is also illustrated by their use in codebook design required for image compression based on vector quantization