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118
Voted
ICONIP
2007
15 years 2 months ago
2007
Variational methods for approximate inference in machine learning often adapt a parametric probability distribution to optimize a given objective function. This view is especially ...
107
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AAAI
2012
13 years 3 months ago
2012
Many noise models do not faithfully reflect the noise processes introduced during data collection in many real-world applications. In particular, we argue that a type of noise re...
ICML
2006
IEEE
16 years 1 months ago
2006
IEEE
Principal components and canonical correlations are at the root of many exploratory data mining techniques and provide standard pre-processing tools in machine learning. Lately, p...
107
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ICANN
2010
Springer
15 years 26 days ago
2010
Springer
We discuss the problem of overfitting of probabilistic neural networks in the framework of statistical pattern recognition. The probabilistic approach to neural networks provides a...
125
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ICPR
2010
IEEE
14 years 11 months ago
2010
IEEE
Abstract—Learning dynamical systems is one of the important problems in many fields. In this paper, we present an algorithm for learning non-linear dynamical systems which works...