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» Semi-Supervised Learning of Mixture Models
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ICML
2007
IEEE
16 years 4 months ago
Robust mixtures in the presence of measurement errors
We develop a mixture-based approach to robust density modeling and outlier detection for experimental multivariate data that includes measurement error information. Our model is d...
Ata Kabán, Jianyong Sun, Somak Raychaudhury
CSDA
2007
134views more  CSDA 2007»
15 years 3 months ago
Variational approximations in Bayesian model selection for finite mixture distributions
Variational methods for model comparison have become popular in the neural computing/machine learning literature. In this paper we explore their application to the Bayesian analys...
Clare A. McGrory, D. M. Titterington
NECO
1998
119views more  NECO 1998»
15 years 2 months ago
Density Estimation by Mixture Models with Smoothing Priors
In the statistical approach for self-organizing maps (SOMs), learning is regarded as an estimation algorithm for a Gaussian mixture model with a Gaussian smoothing prior on the ce...
Akio Utsugi
132
Voted
SIGIR
2004
ACM
15 years 8 months ago
A two-stage mixture model for pseudo feedback
Pseudo feedback is a commonly used technique to improve information retrieval performance. It assumes a few top-ranked documents to be relevant, and learns from them to improve th...
Tao Tao, ChengXiang Zhai
MLMI
2004
Springer
15 years 8 months ago
Mixture of SVMs for Face Class Modeling
We 1 present a method for face detection which uses a new SVM structure trained in an expert manner in the eigenface space. This robust method has been introduced as a post process...
Julien Meynet, Vlad Popovici, Jean-Philippe Thiran