509 search results - page 53 / 102 » Using Learning for Approximation in Stochastic Processes |
122
Voted
UAI
2003
15 years 3 months ago
2003
Continuous time Bayesian networks (CTBN) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cycli...
133
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ECML
2007
Springer
15 years 6 months ago
2007
Springer
Abstract. Ensemble methods are popular learning methods that usually increase the predictive accuracy of a classifier though at the cost of interpretability and insight in the deci...
112
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TNN
1998
15 years 2 months ago
1998
In this paper we regard first the situation where parallel channels are disturbed by noise. With the goal of maximal information conservation we deduce the conditions for a transf...
116
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ICA
2007
Springer
15 years 8 months ago
2007
Springer
We have derived a new algorithm for dictionary learning for sparse coding in the ℓ1 exact sparse framework. The algorithm does not rely on an approximation residual to operate, b...
115
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ICDAR
2009
IEEE
15 years 9 months ago
2009
IEEE
In this paper, we present a new incremental learning strategy for handwritten character recognition systems. This learning strategy enables the recognition system to learn “rapi...