11 search results - page 1 / 3 » Nightmare at test time: robust learning by feature deletion |
ICML
2006
IEEE
14 years 6 months ago
2006
IEEE
When constructing a classifier from labeled data, it is important not to assign too much weight to any single input feature, in order to increase the robustness of the classifier....
UAI
2003
13 years 6 months ago
2003
This paper considers a method that combines ideas from Bayesian learning, Bayesian network inference, and classical hypothesis testing to produce a more reliable and robust test o...
RECOMB
2008
Springer
14 years 5 months ago
2008
Springer
We developed Gr?mlin 2.0, a new multiple network aligner with (1) a novel scoring function that can use arbitrary features of a multiple network alignment, such as protein deletion...
ICRA
2008
IEEE
13 years 11 months ago
2008
IEEE
— Localization and context interpretation are two key competences for mobile robot systems. Visual place recognition, as opposed to purely geometrical models, holds promise of hi...
DIS
2006
Springer
13 years 7 months ago
2006
Springer
Knowledge discovery systems are constrained by three main limited resources: time, memory and sample size. Sample size is traditionally the dominant limitation, but in many present...