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» The Set Covering Machine
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124
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NIPS
2007
15 years 4 months ago
Random Features for Large-Scale Kernel Machines
To accelerate the training of kernel machines, we propose to map the input data to a randomized low-dimensional feature space and then apply existing fast linear methods. The feat...
Ali Rahimi, Benjamin Recht
137
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MSCS
2010
104views more  MSCS 2010»
15 years 1 months ago
A note on accelerated Turing machines
ABSTRACT. In this note we prove that any Turing machine which uses only a finite computational space for every input cannot solve an uncomputable problem even in case it runs in a...
Cristian S. Calude, Ludwig Staiger
118
Voted
CVPR
2008
IEEE
16 years 4 months ago
Margin-based discriminant dimensionality reduction for visual recognition
Nearest neighbour classifiers and related kernel methods often perform poorly in high dimensional problems because it is infeasible to include enough training samples to cover the...
Hakan Cevikalp, Bill Triggs, Frédéri...
120
Voted
FSKD
2007
Springer
98views Fuzzy Logic» more  FSKD 2007»
15 years 8 months ago
Learning Selective Averaged One-Dependence Estimators for Probability Estimation
Naïve Bayes is a well-known effective and efficient classification algorithm, but its probability estimation performance is poor. Averaged One-Dependence Estimators, simply AODE,...
Qing Wang, Chuan-hua Zhou, Jiankui Guo
RAID
2004
Springer
15 years 8 months ago
HoneyStat: Local Worm Detection Using Honeypots
Worm detection systems have traditionally used global strategies and focused on scan rates. The noise associated with this approach requires statistical techniques and large data s...
David Dagon, Xinzhou Qin, Guofei Gu, Wenke Lee, Ju...