BYY harmony learning, structural RPCL, and topological self-organizing on mixture models
Neural Networks - New developments in self-organizing maps
Fast learning in networks of locally-tuned processing units
Neural Computation
A gradient BYY harmony learning algorithm on mixture of experts for curve detection
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
An overview of statistical learning theory
IEEE Transactions on Neural Networks
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The well-known mixtures of experts(ME) model is usually trained by expectation maximization(EM) algorithm for maximum likelihood learning. However, we have to first determine the number of experts, which is often hardly known. Derived from regularization theory, a regularized minimum cross-entropy(RMCE) algorithm is proposed to train ME model, which can automatically make model selection. When time series is modeled by ME, it is demonstrated by some climate prediction experiments that RMCE algorithm outperforms EM algorithm. We also compare RMCE algorithm with other regression methods such as back-propagation(BP) algorithm and normalized radial basis function(NRBF) network, and find that RMCE algorithm still shows promising results.