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» Graph model selection using maximum likelihood
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DICTA
2003
13 years 8 months ago
Maximum-Likelihood Circle-Parameter Estimation via Convolution
In this paper, we present an interpretation of the Maximum Likelihood Estimator (MLE) and the Delogne-K˚asa Estimator (DKE) for circle-parameter estimation via convolution. Under ...
Emanuel Zelniker, Vaughan Clarkson
ESCIENCE
2007
IEEE
14 years 1 months ago
Distributed and Generic Maximum Likelihood Evaluation
This paper presents GMLE 1 , a generic and distributed framework for maximum likelihood evaluation. GMLE is currently being applied to astroinformatics for determining the shape o...
Travis J. Desell, Nathan Cole, Malik Magdon-Ismail...
ICML
2005
IEEE
14 years 8 months ago
Supervised dimensionality reduction using mixture models
Given a classification problem, our goal is to find a low-dimensional linear transformation of the feature vectors which retains information needed to predict the class labels. We...
Sajama, Alon Orlitsky
CORR
2010
Springer
228views Education» more  CORR 2010»
13 years 6 months ago
Sparse Inverse Covariance Selection via Alternating Linearization Methods
Gaussian graphical models are of great interest in statistical learning. Because the conditional independencies between different nodes correspond to zero entries in the inverse c...
Katya Scheinberg, Shiqian Ma, Donald Goldfarb
UAI
2003
13 years 8 months ago
Bayesian Hierarchical Mixtures of Experts
The Hierarchical Mixture of Experts (HME) is a well-known tree-structured model for regression and classification, based on soft probabilistic splits of the input space. In its o...
Christopher M. Bishop, Markus Svensén