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» Learning from Highly Structured Data by Decomposition
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BMCBI
2010
109views more  BMCBI 2010»
14 years 10 months ago
Structural alphabets derived from attractors in conformational space
Background: The hierarchical and partially redundant nature of protein structures justifies the definition of frequently occurring conformations of short fragments as `states'...
Alessandro Pandini, Arianna Fornili, Jens Kleinjun...
AES
2008
Springer
111views Cryptology» more  AES 2008»
14 years 10 months ago
Ambient vibration testing and structural evaluation of an historic suspension footbridge
The paper summarises the ambient vibration-based assessment of an historic suspension footbridge, dating back to 1928. The footbridge is characterised by the timber floor system s...
C. Gentile, N. Gallino
ECCV
2000
Springer
15 years 2 months ago
Factorization with Uncertainty
Factorization using Singular Value Decomposition (SVD) is often used for recovering 3D shape and motion from feature correspondences across multiple views. SVD is powerful at find...
Michal Irani, P. Anandan
ICML
2008
IEEE
15 years 10 months ago
Composite kernel learning
The Support Vector Machine (SVM) is an acknowledged powerful tool for building classifiers, but it lacks flexibility, in the sense that the kernel is chosen prior to learning. Mul...
Marie Szafranski, Yves Grandvalet, Alain Rakotomam...
VIS
2009
IEEE
246views Visualization» more  VIS 2009»
15 years 11 months ago
Supercubes: A High-Level Primitive for Diamond Hierarchies
Abstract-- Volumetric datasets are often modeled using a multiresolution approach based on a nested decomposition of the domain into a polyhedral mesh. Nested tetrahedral meshes ge...
Kenneth Weiss, Leila De Floriani