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» Using Machine Learning to Focus Iterative Optimization
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ML
2008
ACM
222views Machine Learning» more  ML 2008»
15 years 3 months ago
Boosted Bayesian network classifiers
The use of Bayesian networks for classification problems has received significant recent attention. Although computationally efficient, the standard maximum likelihood learning me...
Yushi Jing, Vladimir Pavlovic, James M. Rehg
AUSAI
2001
Springer
15 years 7 months ago
Wrapping Boosters against Noise
Abstract. Wrappers have recently been used to obtain parameter optimizations for learning algorithms. In this paper we investigate the use of a wrapper for estimating the correct n...
Bernhard Pfahringer, Geoffrey Holmes, Gabi Schmidb...
ISMB
1994
15 years 4 months ago
Stochastic Motif Extraction Using Hidden Markov Model
In this paper, westudy the application of an ttMM(hidden Markov model) to the problem of representing protein sequencesby a stochastic motif. Astochastic protein motif represents ...
Yukiko Fujiwara, Minoru Asogawa, Akihiko Konagaya
ALT
2010
Springer
15 years 4 months ago
Consistency of Feature Markov Processes
We are studying long term sequence prediction (forecasting). We approach this by investigating criteria for choosing a compact useful state representation. The state is supposed t...
Peter Sunehag, Marcus Hutter
GECCO
2011
Springer
236views Optimization» more  GECCO 2011»
14 years 6 months ago
Online, GA based mixture of experts: a probabilistic model of ucs
In recent years there have been efforts to develop a probabilistic framework to explain the workings of a Learning Classifier System. This direction of research has met with lim...
Narayanan Unny Edakunni, Gavin Brown, Tim Kovacs