A hierarchical genetic algorithm for segmentation of multi-spectral human-brain MRI
Expert Systems with Applications: An International Journal
Rough fuzzy set-based image compression
Fuzzy Sets and Systems
Iterative Bayesian fuzzy clustering toward flexible icon-based assistive software for the disabled
Information Sciences: an International Journal
Clustering: A neural network approach
Neural Networks
Suppressed fuzzy-soft learning vector quantization for MRI segmentation
Artificial Intelligence in Medicine
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
A clustering algorithm using the ordered weight sum of self-organizing feature maps
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part III
Reformulating Learning Vector Quantization and Radial Basis Neural Networks
Fundamenta Informaticae
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This paper presents a general methodology for the development of fuzzy algorithms for learning vector quantization (FALVQ). The design of specific FALVQ algorithms according to existing approaches reduces to the selection of the membership function assigned to the weight vectors of an LVQ competitive neural network, which represent the prototypes. The development of a broad variety of FALVQ algorithms can be accomplished by selecting the form of the interference function that determines the effect of the nonwinning prototypes on the attraction between the winning prototype and the input of the network. The proposed methodology provides the basis for extending the existing FALVQ 1, FALVQ 2, and FALVQ 3 families of algorithms. This paper also introduces two quantitative measures which establish a relationship between the formulation that led to FALVQ algorithms and the competition between the prototypes during the learning process. The proposed algorithms and competition measures are tested and evaluated using the IRIS data set. The significance of the proposed competition measure is illustrated using FALVQ algorithms to perform segmentation of magnetic resonance images of the brain