Entropy Regularized Likelihood Learning on Gaussian Mixture: Two Gradient Implementations for Automatic Model Selection

In Gaussian mixture modeling, it is crucial to select the number of Gaussians or mixture model for a sample data set. Under regularization theory, we aim to solve this kind of model selection problem through implementing entropy regularized likelihood (ERL) learning on Gaussian mixture via a batch g...

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Bibliographic Details
Published in:	Neural processing letters Vol. 25; no. 1; pp. 17 - 30
Main Author:	Lu, Zhiwu
Format:	Journal Article
Language:	English
Published:	Dordrecht Springer 01.02.2007 Springer Nature B.V
Subjects:	Algorithms Applied sciences Artificial intelligence Computer science; control theory; systems Datasets Entropy Exact sciences and technology Machine learning Parameters Probabilistic models Regularization Mixed distribution regularization theory Density estimation Parameter estimation Gradient Mechanism analysis Automatic selection model selection Modeling Adaptive method Search algorithm Selection problem Gaussian process Batch process Batch production competitive learning Gaussian mixture System identification Learning algorithm Artificial intelligence
ISSN:	1370-4621, 1573-773X
Online Access:	Get full text
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