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TNN
2010
176views Management» more  TNN 2010»
14 years 11 months ago
Sparse approximation through boosting for learning large scale kernel machines
Abstract--Recently, sparse approximation has become a preferred method for learning large scale kernel machines. This technique attempts to represent the solution with only a subse...
Ping Sun, Xin Yao
CVPR
2010
IEEE
14 years 1 months ago
Abrupt motion tracking via adaptive stochastic approximation Monte Carlo sampling
Robust tracking of abrupt motion is a challenging task in computer vision due to the large motion uncertainty. In this paper, we propose a stochastic approximation Monte Carlo (...
Xiuzhuang Zhou and Yao Lu
ESA
1993
Springer
140views Algorithms» more  ESA 1993»
15 years 8 months ago
Fully Dynamic Algorithms for Bin Packing: Being (mostly) Myopic Helps
The problem of maintaining an approximate solution for one-dimensional bin packing when items may arrive and depart dynamically is studied. In accordance with various work on fully...
Zoran Ivkovic, Errol L. Lloyd
STOC
2004
ACM
126views Algorithms» more  STOC 2004»
16 years 5 months ago
Bypassing the embedding: algorithms for low dimensional metrics
The doubling dimension of a metric is the smallest k such that any ball of radius 2r can be covered using 2k balls of raThis concept for abstract metrics has been proposed as a na...
Kunal Talwar
CC
2007
Springer
157views System Software» more  CC 2007»
15 years 11 months ago
New Algorithms for SIMD Alignment
Optimizing programs for modern multiprocessor or vector platforms is a major important challenge for compilers today. In this work, we focus on one challenging aspect: the SIMD ALI...
Liza Fireman, Erez Petrank, Ayal Zaks