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» Optimization of Convex Risk Functions
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NIPS
2007
14 years 5 days ago
Bundle Methods for Machine Learning
We present a globally convergent method for regularized risk minimization problems. Our method applies to Support Vector estimation, regression, Gaussian Processes, and any other ...
Alex J. Smola, S. V. N. Vishwanathan, Quoc V. Le
CASC
2010
Springer
151views Mathematics» more  CASC 2010»
13 years 9 months ago
Supporting Global Numerical Optimization of Rational Functions by Generic Symbolic Convexity Tests
Convexity is an important property in nonlinear optimization since it allows to apply efficient local methods for finding global solutions. We propose to apply symbolic methods t...
Winfried Neun, Thomas Sturm, Stefan Vigerske
PAMI
2011
13 years 5 months ago
Multiview Stereo and Silhouette Consistency via Convex Functionals over Convex Domains
—We propose a convex formulation for silhouette and stereo fusion in 3D reconstruction from multiple images. The key idea is to show that the reconstruction problem can be cast a...
Daniel Cremers, Kalin Kolev
OL
2010
104views more  OL 2010»
13 years 9 months ago
On representations of the feasible set in convex optimization
We consider the convex optimization problem minx{f(x) : gj(x) ≤ 0, j = 1, . . . , m} where f is convex, the feasible set K is convex and Slater’s condition holds, but the funct...
Jean B. Lasserre
ISIPTA
2003
IEEE
111views Mathematics» more  ISIPTA 2003»
14 years 4 months ago
Convex Imprecise Previsions: Basic Issues and Applications
In this paper we study two classes of imprecise previsions, which we termed convex and centered convex previsions, in the framework of Walley’s theory of imprecise previsions. W...
Renato Pelessoni, Paolo Vicig