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» Approximating Component Selection
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UAI
1996
14 years 11 months ago
Asymptotic Model Selection for Directed Networks with Hidden Variables
We extend the Bayesian Information Criterion (BIC), an asymptotic approximation for the marginal likelihood, to Bayesian networks with hidden variables. This approximation can be ...
Dan Geiger, David Heckerman, Christopher Meek
CORR
2002
Springer
132views Education» more  CORR 2002»
14 years 9 months ago
Robust Feature Selection by Mutual Information Distributions
Mutual information is widely used in artificial intelligence, in a descriptive way, to measure the stochastic dependence of discrete random variables. In order to address question...
Marco Zaffalon, Marcus Hutter
ICANN
2009
Springer
15 years 4 months ago
Modelling Image Complexity by Independent Component Analysis, with Application to Content-Based Image Retrieval
Abstract. Estimating the degree of similarity between images is a challenging task as the similarity always depends on the context. Because of this context dependency, it seems qui...
Jukka Perkiö, Aapo Hyvärinen
IJCNN
2007
IEEE
15 years 4 months ago
Branching Principal Components: Elastic Graphs, Topological Grammars and Metro Maps
— To approximate complex data, we propose new type of low-dimensional “principal object”: principal cubic complex. This complex is a generalization of linear and nonlinear pr...
Alexander N. Gorban, Neil R. Sumner, Andrei Yu. Zi...
ICCD
2005
IEEE
116views Hardware» more  ICCD 2005»
15 years 6 months ago
Enhanced Dual-Transition Probabilistic Power Estimation with Selective Supergate Analysis
Consideration of pairs of transition in probabilistic simulation allows power estimation for digital circuits in which inertial delays can filter glitches [5]. However, the merit ...
Fei Hu, Vishwani D. Agrawal