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» A Greedy EM Algorithm for Gaussian Mixture Learning
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NIPS
2004
14 years 11 months ago
Learning Gaussian Process Kernels via Hierarchical Bayes
We present a novel method for learning with Gaussian process regression in a hierarchical Bayesian framework. In a first step, kernel matrices on a fixed set of input points are l...
Anton Schwaighofer, Volker Tresp, Kai Yu
ICIP
2009
IEEE
15 years 10 months ago
Joint Recovery And Segmentation Of Polarimetric Images Using A Compound Mrf And Mixture Modeling
We propose a new approach for the restoration of polarimetric Stokes images, capable of simultaneously segmenting and restoring the images. In order to easily handle the admissibi...
TIP
2002
179views more  TIP 2002»
14 years 9 months ago
Unsupervised image classification, segmentation, and enhancement using ICA mixture models
An unsupervised classification algorithm is derived by modeling observed data as a mixture of several mutually exclusive classes that are each described by linear combinations of i...
Te-Won Lee, Michael S. Lewicki
ICML
2003
IEEE
15 years 10 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...
FGR
2004
IEEE
167views Biometrics» more  FGR 2004»
15 years 1 months ago
Adaptive Learning of an Accurate Skin-Color Model
Due to variations of lighting conditions, camera hardware settings, and the range of skin coloration among human beings, a pre-defined skin-color model cannot accurately capture t...
Qiang Zhu, Kwang-Ting Cheng, Ching-Tung Wu, Yi-Leh...