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ICML
2000
IEEE
14 years 11 months ago
FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness
Most machine learning algorithms are lazy: they extract from the training set the minimum information needed to predict its labels. Unfortunately, this often leads to models that ...
Joseph O'Sullivan, John Langford, Rich Caruana, Av...
GECCO
2005
Springer
175views Optimization» more  GECCO 2005»
14 years 3 months ago
Inverse multi-objective robust evolutionary design optimization in the presence of uncertainty
In many real-world design problems, uncertainties are often present and practically impossible to avoid. Many existing works on Evolutionary Algorithm (EA) for handling uncertaint...
Dudy Lim, Yew-Soon Ong, Bu-Sung Lee
INFOCOM
2003
IEEE
14 years 3 months ago
Robust Location Detection in Emergency Sensor Networks
— We propose a new framework for providing robust location detection in emergency response systems, based on the theory of identifying codes. The key idea of this approach is to ...
Saikat Ray, Rachanee Ungrangsi, Francesco De Pelle...
GECCO
2006
Springer
181views Optimization» more  GECCO 2006»
14 years 1 months ago
Robustness in cooperative coevolution
Though recent analysis of traditional cooperative coevolutionary algorithms (CCEAs) casts doubt on their suitability for static optimization tasks, our experience is that the algo...
R. Paul Wiegand, Mitchell A. Potter
TSMC
2008
99views more  TSMC 2008»
13 years 10 months ago
Robust Regularized Kernel Regression
Robust regression techniques are critical to fitting data with noise in real-world applications. Most previous work of robust kernel regression is usually formulated into a dual fo...
Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu