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» Margin based feature selection - theory and algorithms
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MM
2009
ACM
185views Multimedia» more  MM 2009»
14 years 2 months ago
Deep exploration for experiential image retrieval
Experiential image retrieval systems aim to provide the user with a natural and intuitive search experience. The goal is to empower the user to navigate large collections based on...
Bart Thomee, Mark J. Huiskes, Erwin M. Bakker, Mic...
3DOR
2010
13 years 2 months ago
Learning the Compositional Structure of Man-Made Objects for 3D Shape Retrieval
While approaches based on local features play a more and more important role for 3D shape retrieval, the problems of feature selection and similarity measurement between sets of l...
Raoul Wessel, Reinhard Klein
ECCV
2010
Springer
14 years 1 months ago
Robust Multi-View Boosting with Priors
Many learning tasks for computer vision problems can be described by multiple views or multiple features. These views can be exploited in order to learn from unlabeled data, a.k.a....
VLDB
2007
ACM
103views Database» more  VLDB 2007»
14 years 8 months ago
On the Production of Anorexic Plan Diagrams
A "plan diagram" is a pictorial enumeration of the execution plan choices of a database query optimizer over the relational selectivity space. We have shown recently tha...
Harish D., Pooja N. Darera, Jayant R. Haritsa
GECCO
2008
Springer
183views Optimization» more  GECCO 2008»
13 years 8 months ago
UMDAs for dynamic optimization problems
This paper investigates how the Univariate Marginal Distribution Algorithm (UMDA) behaves in non-stationary environments when engaging in sampling and selection strategies designe...
Carlos M. Fernandes, Cláudio F. Lima, Agost...