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Efficient global maximum likelihood estimation through kernel methods
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DOI：10.1016/j.neunet.2010.03.003.png)
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Abstract
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A new efficient technique for estimating probability densities from data through the application of the approximate global maximum likelihood (AGML) approach is proposed. It employs a composition of kernel functions to estimate the correct behavior of parameters involved in the expression of the unknown probability density. Convergence to the optimal solution is guaranteed by a deterministic learning framework when low discrepancy sequences are used to generate the centers of the kernels. Trials on mixture of Gaussians show that the proposed semi-local technique is able to efficiently approximate the maximum likelihood solution even in complex situations where implements lions based on standard neural networks require an excessive computational cost. (C) 2010 Elsevier Ltd. All rights reserved.
Keywords:
Maximum likelihood estimation
Deterministic learning
Kernel models
Low-discrepancy sequences
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