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» Stochastic methods for l1 regularized loss minimization
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NIPS
2001
15 years 1 months ago
Online Learning with Kernels
Abstract--Kernel-based algorithms such as support vector machines have achieved considerable success in various problems in batch setting, where all of the training data is availab...
Jyrki Kivinen, Alex J. Smola, Robert C. Williamson
98
Voted
CORR
2008
Springer
186views Education» more  CORR 2008»
15 years 14 days ago
Greedy Signal Recovery Review
The two major approaches to sparse recovery are L1-minimization and greedy methods. Recently, Needell and Vershynin developed Regularized Orthogonal Matching Pursuit (ROMP) that ha...
Deanna Needell, Joel A. Tropp, Roman Vershynin
90
Voted
SIAMNUM
2010
140views more  SIAMNUM 2010»
14 years 7 months ago
Finite Element Approximation of the Linear Stochastic Wave Equation with Additive Noise
Semidiscrete finite element approximation of the linear stochastic wave equation with additive noise is studied in a semigroup framework. Optimal error estimates for the determinis...
Mihály Kovács, Stig Larsson, Fardin ...
JMLR
2006
143views more  JMLR 2006»
15 years 11 days ago
Consistency and Convergence Rates of One-Class SVMs and Related Algorithms
We determine the asymptotic behaviour of the function computed by support vector machines (SVM) and related algorithms that minimize a regularized empirical convex loss function i...
Régis Vert, Jean-Philippe Vert
100
Voted
ICCV
2007
IEEE
16 years 2 months ago
A Globally Optimal Algorithm for Robust TV-L1 Range Image Integration
Robust integration of range images is an important task for building high-quality 3D models. Since range images, and in particular range maps from stereo vision, may have a substa...
Christopher Zach, Thomas Pock, Horst Bischof