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ICML
2003
IEEE
14 years 8 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...
ICASSP
2007
IEEE
14 years 2 months ago
Query by Example of Audio Signals using Euclidean Distance Between Gaussian Mixture Models
Query by example of multimedia signals aims at automatic retrieval of media samples from a database, which are similar to a userprovided example. This paper proposes a method for ...
Marko Helén, Tuomas Virtanen
IJCNN
2006
IEEE
14 years 1 months ago
Automated Model Selection (AMS) on Finite Mixtures: A Theoretical Analysis
— From the Bayesian Ying-Yang (BYY) harmony learning theory, a harmony function has been developed for finite mixtures with a novel property that its maximization can make model...
Jinwen Ma
ICIP
2010
IEEE
13 years 5 months ago
Gaussian mixture models for spots in microscopy using a new split/merge em algorithm
In confocal microscopy imaging, target objects are labeled with fluorescent markers in the living specimen, and usually appear as spots in the observed images. Spot detection and ...
Kangyu Pan, Anil C. Kokaram, Jens Hillebrand, Mani...
ICPR
2010
IEEE
13 years 5 months ago
Information Theoretic Expectation Maximization Based Gaussian Mixture Modeling for Speaker Verification
The expectation maximization (EM) algorithm is widely used in the Gaussian mixture model (GMM) as the state-of-art statistical modeling technique. Like the classical EM method, th...
Sheeraz Memon, Margaret Lech, Namunu Chinthaka Mad...