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CGF
2005
252views more  CGF 2005»
13 years 7 months ago
Support Vector Machines for 3D Shape Processing
We propose statistical learning methods for approximating implicit surfaces and computing dense 3D deformation fields. Our approach is based on Support Vector (SV) Machines, which...
Florian Steinke, Bernhard Schölkopf, Volker B...
BMCBI
2004
125views more  BMCBI 2004»
13 years 7 months ago
A functional hierarchical organization of the protein sequence space
Background: It is a major challenge of computational biology to provide a comprehensive functional classification of all known proteins. Most existing methods seek recurrent patte...
Noam Kaplan, Moriah Friedlich, Menachem Fromer, Mi...
CVPR
2009
IEEE
15 years 2 months ago
Contextual Classification with Functional Max-Margin Markov Networks
We address the problem of label assignment in computer vision: given a novel 3-D or 2-D scene, we wish to assign a unique label to every site (voxel, pixel, superpixel, etc.). To...
Daniel Munoz, James A. Bagnell, Martial Hebert, Ni...
IJCNN
2007
IEEE
14 years 1 months ago
Agnostic Learning versus Prior Knowledge in the Design of Kernel Machines
Abstract— The optimal model parameters of a kernel machine are typically given by the solution of a convex optimisation problem with a single global optimum. Obtaining the best p...
Gavin C. Cawley, Nicola L. C. Talbot
BMCBI
2010
202views more  BMCBI 2010»
13 years 7 months ago
NeMo: Network Module identification in Cytoscape
Background: As the size of the known human interactome grows, biologists increasingly rely on computational tools to identify patterns that represent protein complexes and pathway...
Corban G. Rivera, Rachit Vakil, Joel S. Bader