BYY harmony learning, structural RPCL, and topological self-organizing on mixture models
Neural Networks - New developments in self-organizing maps
A dynamic merge-or-split learning algorithm on gaussian mixture for automated model selection
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
A Semi-supervised Learning Algorithm on Gaussian Mixture with Automatic Model Selection
Neural Processing Letters
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Gaussian models and fast learning algorithm for persistence analysis of tracked video objects
HSI'09 Proceedings of the 2nd conference on Human System Interactions
A generalized competitive learning algorithm on gaussian mixture with automatic model selection
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
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As for Gaussian mixture modeling, the key problem is to select the number of Gaussians in the mixture. Based on regularization theory, we aim to make this kind of model selection by implementing an iterative algorithm for entropy regularized likelihood (ERL) learning on Gaussian mixture. The simulation experiments have demonstrated that the ERL algorithm can automatically detect the number of Gaussians with a good estimation of the parameters in the original mixture, even on a sample set with a high degree of overlap. Moreover, the ERL algorithm also leads to a promising result when applied to the classification of iris data.