Elements of information theory
Elements of information theory
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Kernel-based equiprobabilistic topographic map formation
Neural Computation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Numerical Recipes in C: The Art of Scientific Computing
Numerical Recipes in C: The Art of Scientific Computing
Kernel-based topographic map formation by local density modeling
Neural Computation
Neyman-Pearson Neural Detectors
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
Kernel-based topographic map formation achieved with an information-theoretic approach
Neural Networks - New developments in self-organizing maps
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Maximum Likelihood Topographic Map Formation
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
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A new log-likelihood (LL) based metric for goodness-of-fit testing and monitoring unsupervised learning of mixture densities is introduced, called differential LL. We develop the metric in the case of a Gaussian kernel fitted to a Gaussian distribution. We suggest a possible differential LL learning strategy, show the formal link with the Kullback-Leibler divergence and the quantization error, and introduce a Gaussian factorial distribution approximation by subspaces.