A new curve detection method: randomized Hough transform (RHT)
Pattern Recognition Letters
Constrained Hough transforms for curve detection
Computer Vision and Image Understanding
BYY harmony learning, structural RPCL, and topological self-organizing on mixture models
Neural Networks - New developments in self-organizing maps
Strip line detection and thinning by RPCL-based local PCA
Pattern Recognition Letters
Adaptive mixtures of local experts
Neural Computation
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
A Semi-supervised Learning Algorithm on Gaussian Mixture with Automatic Model Selection
Neural Processing Letters
Implementation of artificial intelligence in the time series prediction problem
SMO'06 Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization
Comparison of artificial intelligence methods for predicting the time series problem
SMO'06 Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Generalized competitive learning of Gaussian mixture models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Entropy regularization, automatic model selection, and unsupervised image segmentation
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
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The well-known mixtures of experts (ME) model has been used in many different areas to account for nonlinearities and other complexities in the data, such as time series prediction. We usually train ME model by expectation maximization (EM) algorithm for maximum likelihood learning. However, the number of experts has to be determined first, 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) and normalized radial basis function (NRBF) networks, and find that RMCE algorithm shows promising results. Moreover, we investigate curve detection problem by ME model with RMCE algorithm, which can detect curves (straight lines or circles) from a binary image. Some simulations and image experiments show that RMCE algorithm can automatically determine the number of straight lines or circles during parameter learning against noise, and in this way our algorithm does better than Hough transform (HT).