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JMLR
2002
74views more  JMLR 2002»
14 years 9 months ago
The Representational Power of Discrete Bayesian Networks
One of the most important fundamental properties of Bayesian networks is the representational power, reflecting what kind of functions they can or cannot represent. In this paper,...
Charles X. Ling, Huajie Zhang
ISIPTA
2003
IEEE
117views Mathematics» more  ISIPTA 2003»
15 years 2 months ago
Dynamic Programming for Discrete-Time Systems with Uncertain Gain
We generalise the optimisation technique of dynamic programming for discretetime systems with an uncertain gain function. We assume that uncertainty about the gain function is des...
Gert de Cooman, Matthias C. M. Troffaes
SODA
2010
ACM
175views Algorithms» more  SODA 2010»
15 years 6 months ago
Lower Bounds for Testing Triangle-freeness in Boolean Functions
Let f1, f2, f3 : Fn 2 {0, 1} be three Boolean functions. We say a triple (x, y, x + y) is a triangle in
Arnab Bhattacharyya, Ning Xie
EUROCRYPT
2006
Springer
15 years 1 months ago
The Function Field Sieve in the Medium Prime Case
In this paper, we study the application of the function field sieve algorithm for computing discrete logarithms over finite fields of the form Fqn when q is a medium-sized prime po...
Antoine Joux, Reynald Lercier
66
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ICDM
2005
IEEE
116views Data Mining» more  ICDM 2005»
15 years 3 months ago
Learning Functional Dependency Networks Based on Genetic Programming
Bayesian Network (BN) is a powerful network model, which represents a set of variables in the domain and provides the probabilistic relationships among them. But BN can handle dis...
Wing-Ho Shum, Kwong-Sak Leung, Man Leung Wong