Neural Computation
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
GTM: the generative topographic mapping
Neural Computation
Bayesian Approaches to Gaussian Mixture Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mixtures of probabilistic principal component analyzers
Neural Computation
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
Bayesian Inference of Noise Levels in Regression
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
On convergence properties of the em algorithm for gaussian mixtures
Neural Computation
Stochastic choice of basis functions in adaptive function approximation and the functional-link net
IEEE Transactions on Neural Networks
Learning Gaussian mixture models with entropy-based criteria
IEEE Transactions on Neural Networks
Entropy-based variational scheme for fast bayes learning of Gaussian mixtures
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Hi-index | 0.00 |
Training probability-density estimating neural networks with the expectation-maximization (EM) algorithm aims to maximize the likelihood of the training set and therefore leads to overfitting for sparse data. In this article, a regularization method for mixture models with generalized linear kernel centers is proposed, which adopts the Bayesian evidence approach and optimizes the hyperparameters of the prior by type II maximum likelihood. This includes a marginalization over the parameters, which is done by Laplace approximation and requires the derivation of the Hessian of the log-likelihood function. The incorporation of this approach into the standard training scheme leads to a modified form of the EM algorithm, which includes a regularization term and adapts the hyperparameters on-line after each EM cycle. The article presents applications of this scheme to classification problems, the prediction of stochastic time series, and latent space models.