GTM: the generative topographic mapping
Neural Computation
Kernel-based equiprobabilistic topographic map formation
Neural Computation
A stochastic self-organizing map for proximity data
Neural Computation
Dynamic topology representing networks
Neural Networks
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Kernel-based topographic map formation by local density modeling
Neural Computation
Clustering based on conditional distributions in an auxiliary space
Neural Computation
Joint entropy maximization in kernel-based topographic maps
Neural Computation
Self-Organization of Topographic Mixture Networks Using Attentional Feedback
Neural Computation
Reclassification as Supervised Clustering
Neural Computation
Neural Computation
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Dynamic self-organizing maps with controlled growth for knowledge discovery
IEEE Transactions on Neural Networks
Ischemia detection with a self-organizing map supplemented by supervised learning
IEEE Transactions on Neural Networks
Efficient and interpretable fuzzy classifiers from data with support vector learning
Intelligent Data Analysis
Efficient and interpretable fuzzy classifiers from data with support vector learning
ICCOMP'05 Proceedings of the 9th WSEAS International Conference on Computers
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Most unsupervised learning algorithms ignore prior application knowledge. Also, Self Orgnanized Maps (SOM) based approaches usually develop topographic maps with disjoint and uniform activation regions that correspond to a hard clustering of the patterns at their nodes. We present a novel Self-Organizing map that adapts its parameters in kernel space, grows dynamically up to a size defined with statistical criteria and is capable of incorporating a priori information in the form of a supervised bias at the cluster formation.