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» Optimization with EM and Expectation-Conjugate-Gradient
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ALMOB
2006
155views more  ALMOB 2006»
13 years 8 months ago
Refining motifs by improving information content scores using neighborhood profile search
The main goal of the motif finding problem is to detect novel, over-represented unknown signals in a set of sequences (e.g. transcription factor binding sites in a genome). The mo...
Chandan K. Reddy, Yao-Chung Weng, Hsiao-Dong Chian...
ICPR
2008
IEEE
14 years 9 months ago
Component-wise parameter smoothing for learning mixture models
In this paper, we propose a novel component-wise smoothing algorithm that constructs a hierarchy (or family) of smoothened log-likelihood surfaces. Our approach first smoothens th...
Bala Rajaratnam, Chandan K. Reddy
ICPR
2008
IEEE
14 years 9 months ago
Exploiting qualitative domain knowledge for learning Bayesian network parameters with incomplete data
When a large amount of data are missing, or when multiple hidden nodes exist, learning parameters in Bayesian networks (BNs) becomes extremely difficult. This paper presents a lea...
Qiang Ji, Wenhui Liao
ICML
2005
IEEE
14 years 8 months ago
Expectation maximization algorithms for conditional likelihoods
We introduce an expectation maximizationtype (EM) algorithm for maximum likelihood optimization of conditional densities. It is applicable to hidden variable models where the dist...
Jarkko Salojärvi, Kai Puolamäki, Samuel ...
EMS
2008
IEEE
13 years 9 months ago
Threat Modeling Revisited: Improving Expressiveness of Attack
Threat modeling plays an important role in the deployment of optimal security controls and a number of threat modeling techniques have been proposed. However, most of the existing...
Drake Patrick Mirembe, Maybin K. Muyeba