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IJCNN
2000
IEEE
15 years 11 months ago
Probabilistic Neural Network Models for Sequential Data
It has already been shown how Artificial Neural Networks (ANNs) can be incorporated into probabilistic models. In this paper we review some of the approaches which have been prop...
Yoshua Bengio
EACL
2003
ACL Anthology
15 years 8 months ago
A Comparison of Event Models for Naive Bayes Anti-Spam E-Mail Filtering
We describe experiments with a Naive Bayes text classifier in the context of anti-spam E-mail filtering, using two different statistical event models: a multi-variate Bernoulli ...
Karl-Michael Schneider
WSC
1997
15 years 8 months ago
Seven Habits of Highly Successful Input Modelers
Discrete-event simulation models typically have stochastic components that mimic the probabilistic nature of the system under consideration. Successful input modeling requires a c...
Lawrence Leemis
PAMI
2008
140views more  PAMI 2008»
15 years 7 months ago
Simplifying Mixture Models Using the Unscented Transform
Mixture of Gaussians (MoG) model is a useful tool in statistical learning. In many learning processes that are based on mixture models, computational requirements are very demandin...
Jacob Goldberger, Hayit Greenspan, Jeremie Dreyfus...
ICASSP
2011
IEEE
14 years 11 months ago
Discriminative simplification of mixture models
Simplification of mixture models has recently emerged as an important issue in the field of statistical learning. The heavy computational demands of using large order models dro...
Yossi Bar-Yosef, Yuval Bistritz