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» Simplifying mixture models through function approximation
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SSD
2007
Springer
124views Database» more  SSD 2007»
14 years 1 months ago
Querying Objects Modeled by Arbitrary Probability Distributions
In many modern applications such as biometric identification systems, sensor networks, medical imaging, geology, and multimedia databases, the data objects are not described exact...
Christian Böhm, Peter Kunath, Alexey Pryakhin...
CORR
2007
Springer
128views Education» more  CORR 2007»
13 years 7 months ago
Model Selection Through Sparse Maximum Likelihood Estimation
We consider the problem of estimating the parameters of a Gaussian or binary distribution in such a way that the resulting undirected graphical model is sparse. Our approach is to...
Onureena Banerjee, Laurent El Ghaoui, Alexandre d'...
NN
1998
Springer
177views Neural Networks» more  NN 1998»
13 years 7 months ago
Soft vector quantization and the EM algorithm
The relation between hard c-means (HCM), fuzzy c-means (FCM), fuzzy learning vector quantization (FLVQ), soft competition scheme (SCS) of Yair et al. (1992) and probabilistic Gaus...
Ethem Alpaydin
ICASSP
2010
IEEE
13 years 7 months ago
Direct importance estimation with probabilistic principal component analyzers
The importance estimation problem (estimating the ratio of two probability density functions) has recently gathered a great deal of attention for use in various applications, e.g....
Makoto Yamada, Masashi Sugiyama, Gordon Wichern
SARA
2005
Springer
14 years 1 months ago
Approximate Model-Based Diagnosis Using Preference-Based Compilation
Abstract. This article introduces a technique for improving the efficiency of diagnosis through approximate compilation. We extend the approach of compiling a diagnostic model, as...
Gregory M. Provan