Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Competitive learning algorithms for vector quantization
Neural Networks
Kernel-based equiprobabilistic topographic map formation
Neural Computation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Self-Organizing Maps
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Controlling the magnification factor of self-organizing feature maps
Neural Computation
Codeword distribution for frequency sensitive competitive learning with one-dimensional input data
IEEE Transactions on Neural Networks
Comparative analysis of fuzzy ART and ART-2A network clustering performance
IEEE Transactions on Neural Networks
Density-based clustering with topographic maps
IEEE Transactions on Neural Networks
Circular backpropagation networks embed vector quantization
IEEE Transactions on Neural Networks
Asymptotic level density in topological feature maps
IEEE Transactions on Neural Networks
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A new approach to density-based clustering and unsupervised classification is introduced for topographic maps. By virtue of our topographic map learning rule, called the kernel-based Maximum Entropy learning Rule (kMER), all neurons have an equal probability to be active (equiprobabilistic map) and, in addition, pilot density estimates are obtained that are compatible with the variable kernel density estimation method. The neurons receive their cluster labels by performing hill-climbing on the density estimates which are located at the neuron weights only. Several methods are suggested and explored for determining the cluster boundaries and the clustering performance is tested for the case where the cluster regions are used for unsupervised classification purposes. Finally, the difference is indicated between (Gaussian) kernel-based density modeling with kMER, and (Gaussian) mixture modeling with maximum likelihood learning.