The nature of statistical learning theory
The nature of statistical learning theory
Kernel-based equiprobabilistic topographic map formation
Neural Computation
Self-Organizing Maps
Joint entropy maximization in kernel-based topographic maps
Neural Computation
Self-organizing mixture networks for probability density estimation
IEEE Transactions on Neural Networks
Self-organizing maps, vector quantization, and mixture modeling
IEEE Transactions on Neural Networks
Differential Log Likelihood for Evaluating and Learning Gaussian Mixtures
Neural Computation
Topographic map formation of factorized Edgeworth-expanded kernels
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Mixture density modeling, Kullback-Leibler divergence, and differential log-likelihood
Signal Processing - Special issue: Information theoretic signal processing
Model-based clustering by probabilistic self-organizing maps
IEEE Transactions on Neural Networks
Probabilistic PCA self-organizing maps
IEEE Transactions on Neural Networks
Multivariate Student-t self-organizing maps
Neural Networks
Directly optimizing topology-preserving maps with evolutionary algorithms
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Probabilistic self-organizing maps for qualitative data
Neural Networks
Probabilistic self-organizing maps for continuous data
IEEE Transactions on Neural Networks
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We introduce a new unsupervised learning algorithm for kernel-based topographic map formation of heteroscedastic gaussian mixtures that allows for a unified account of distortion error (vector quantization), log-likelihood, and Kullback-Leibler divergence.