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» Machine Learning of Higher Order Programs
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PLDI
1998
ACM
13 years 11 months ago
Improving Performance by Branch Reordering
The conditional branch has long been considered an expensive operation. The relative cost of conditional branches has increased as recently designed machines are now relying on de...
Minghui Yang, Gang-Ryung Uh, David B. Whalley
IEEEPACT
2008
IEEE
14 years 1 months ago
Mars: a MapReduce framework on graphics processors
We design and implement Mars, a MapReduce framework, on graphics processors (GPUs). MapReduce is a distributed programming framework originally proposed by Google for the ease of ...
Bingsheng He, Wenbin Fang, Qiong Luo, Naga K. Govi...
ISESE
2003
IEEE
14 years 24 days ago
An Experimental Evaluation of Inspection and Testing for Detection of Design Faults
The two most common strategies for verification and validation, inspection and testing, are in a controlled experiment evaluated in terms of their fault detection capabilities. Th...
Carina Andersson, Thomas Thelin, Per Runeson, Nina...
COLT
1999
Springer
13 years 11 months ago
Beating the Hold-Out: Bounds for K-fold and Progressive Cross-Validation
The empirical error on a test set, the hold-out estimate, often is a more reliable estimate of generalization error than the observed error on the training set, the training estim...
Avrim Blum, Adam Kalai, John Langford
GECCO
2006
Springer
162views Optimization» more  GECCO 2006»
13 years 11 months ago
Evolutionary learning with kernels: a generic solution for large margin problems
In this paper we embed evolutionary computation into statistical learning theory. First, we outline the connection between large margin optimization and statistical learning and s...
Ingo Mierswa