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» A Bayesian Metric for Evaluating Machine Learning Algorithms
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ICML
2010
IEEE
14 years 11 months ago
On the Consistency of Ranking Algorithms
We present a theoretical analysis of supervised ranking, providing necessary and sufficient conditions for the asymptotic consistency of algorithms based on minimizing a surrogate...
John Duchi, Lester W. Mackey, Michael I. Jordan
DNA
2005
Springer
118views Bioinformatics» more  DNA 2005»
15 years 3 months ago
Molecular Learning of wDNF Formulae
We introduce a class of generalized DNF formulae called wDNF or weighted disjunctive normal form, and present a molecular algorithm that learns a wDNF formula from training example...
Byoung-Tak Zhang, Ha-Young Jang
CVPR
2009
IEEE
16 years 5 months ago
Learning to Track with Multiple Observers
We propose a novel approach to designing algorithms for object tracking based on fusing multiple observation models. As the space of possible observation models is too large for...
Björn Stenger, Roberto Cipolla, Thomas Woodle...
ICRA
2008
IEEE
150views Robotics» more  ICRA 2008»
15 years 4 months ago
A Bayesian approach to empirical local linearization for robotics
— Local linearizations are ubiquitous in the control of robotic systems. Analytical methods, if available, can be used to obtain the linearization, but in complex robotics system...
Jo-Anne Ting, Aaron D'Souza, Sethu Vijayakumar, St...
SIGSOFT
2008
ACM
15 years 10 months ago
Finding programming errors earlier by evaluating runtime monitors ahead-of-time
Runtime monitoring allows programmers to validate, for instance, the proper use of application interfaces. Given a property specification, a runtime monitor tracks appropriate run...
Eric Bodden, Patrick Lam, Laurie J. Hendren