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» The Convergence Rate of AdaBoost
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94
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PKDD
2010
Springer
169views Data Mining» more  PKDD 2010»
14 years 10 months ago
Efficient and Numerically Stable Sparse Learning
We consider the problem of numerical stability and model density growth when training a sparse linear model from massive data. We focus on scalable algorithms that optimize certain...
Sihong Xie, Wei Fan, Olivier Verscheure, Jiangtao ...
133
Voted
ICIP
2009
IEEE
14 years 10 months ago
Total variation projection with first order schemes
This article proposes a new algorithm to compute the projection on the set of images whose total variation is bounded by a constant. The projection is computed through a dual form...
Mohamed-Jalal Fadili, Gabriel Peyré
GLOBECOM
2010
IEEE
14 years 10 months ago
Sensor Placement for Minimum Exposure in Distributed Active Sensing Networks
Distributed active sensing is a new sensing paradigm, where active sensors and passive sensors are distributed in a field, and collaboratively detect and track the objects. "E...
Na Xia, Khuong Vu, Rong Zheng
92
Voted
MOR
2010
115views more  MOR 2010»
14 years 7 months ago
Directional Derivatives of Oblique Reflection Maps
Given an oblique reflection map and functions , Dlim (the space of functions that have left and right limits at every point), the directional derivative () of along , evaluate...
Avi Mandelbaum, Kavita Ramanan
139
Voted
ICASSP
2011
IEEE
14 years 4 months ago
Distributed LCMV beamforming in wireless sensor networks with node-specific desired signals
We consider distributed linearly constrained minimum variance (LCMV) beamforming in a wireless sensor network. Each node computes an LCMV beamformer with node-specific constraint...
Alexander Bertrand, Marc Moonen