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UAI
1998
15 years 7 months ago
Learning Mixtures of DAG Models
We describe computationally efficient methods for learning mixtures in which each component is a directed acyclic graphical model (mixtures of DAGs or MDAGs). We argue that simple...
Bo Thiesson, Christopher Meek, David Maxwell Chick...
ICTAI
2010
IEEE
15 years 3 months ago
Unsupervised Greedy Learning of Finite Mixture Models
This work deals with a new technique for the estimation of the parameters and number of components in a finite mixture model. The learning procedure is performed by means of a expe...
Nicola Greggio, Alexandre Bernardino, Cecilia Lasc...
ICML
2003
IEEE
16 years 7 months ago
Learning Mixture Models with the Latent Maximum Entropy Principle
We present a new approach to estimating mixture models based on a new inference principle we have proposed: the latent maximum entropy principle (LME). LME is different both from ...
Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin...
FLAIRS
2007
15 years 8 months ago
Random Subsets Support Learning a Mixture of Heuristics
Problem solvers, both human and machine, have at their disposal many heuristics that may support effective search. The efficacy of these heuristics, however, varies with the probl...
Smiljana Petrovic, Susan L. Epstein
ICNC
2005
Springer
15 years 12 months ago
Segmentation of SAR Image Using Mixture Multiscale ARMA Network
Abstract. A mixture multiscale autoregressive moving average (ARMA) network is proposed for unsupervised segmentation of synthetic aperture radar (SAR) image. The network combines ...
Haixia Xu, Tian Zheng, Fan Meng