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» Modeling When Connections Are the Problem
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TSP
2010
15 years 1 months ago
Gaussian multiresolution models: exploiting sparse Markov and covariance structure
We consider the problem of learning Gaussian multiresolution (MR) models in which data are only available at the finest scale and the coarser, hidden variables serve both to captu...
Myung Jin Choi, Venkat Chandrasekaran, Alan S. Wil...
BMCBI
2007
168views more  BMCBI 2007»
15 years 6 months ago
Benchmarking consensus model quality assessment for protein fold recognition
Background: Selecting the highest quality 3D model of a protein structure from a number of alternatives remains an important challenge in the field of structural bioinformatics. M...
Liam J. McGuffin
ICIP
2003
IEEE
16 years 8 months ago
Estimation of cardiac phases in echographic images using multiple models
This paper presents an algorithm for tracking the left ventricle in echocardiographic sequences, using multiple models. The use of multiple dynamic models is appropriate since the...
Jacinto C. Nascimento, Jorge S. Marques, Joã...
AIPS
2003
15 years 8 months ago
Localizing Planning with Functional Process Models
In this paper we describe a compromise between generative planning and special-purpose software. Hierarchical functional models are used by an intelligent system to represent its ...
J. William Murdock, Ashok K. Goel
CORR
2006
Springer
144views Education» more  CORR 2006»
15 years 6 months ago
Estimation of linear, non-gaussian causal models in the presence of confounding latent variables
The estimation of linear causal models (also known as structural equation models) from data is a well-known problem which has received much attention in the past. Most previous wo...
Patrik O. Hoyer, Shohei Shimizu, Antti J. Kerminen