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AAAI
1997
15 years 4 months ago
Worst-Case Absolute Loss Bounds for Linear Learning Algorithms
The absolute loss is the absolute difference between the desired and predicted outcome. I demonstrateworst-case upper bounds on the absolute loss for the perceptron algorithm and ...
Tom Bylander
EC
2010
176views ECommerce» more  EC 2010»
15 years 11 days ago
Learning Factorizations in Estimation of Distribution Algorithms Using Affinity Propagation
Estimation of distribution algorithms (EDAs) that use marginal product model factorizations have been widely applied to a broad range of, mainly binary, optimization problems. In ...
Roberto Santana, Pedro Larrañaga, Jos&eacut...
ELPUB
2006
ACM
15 years 9 months ago
Modelling a Layer for Real-Time Management of Interactions in Web Based Distance Learning
In the last few years, the University of Aveiro, Portugal, has been offering several distance learning courses over the Web, using e-learning platforms. Experience showed that dif...
Carlos Sousa Pinto, Fernando M. S. Ramos
ICCV
2009
IEEE
15 years 22 days ago
Bayesian Poisson regression for crowd counting
Poisson regression models the noisy output of a counting function as a Poisson random variable, with a log-mean parameter that is a linear function of the input vector. In this wo...
Antoni B. Chan, Nuno Vasconcelos
KDD
2006
ACM
118views Data Mining» more  KDD 2006»
16 years 3 months ago
Mining for proposal reviewers: lessons learned at the national science foundation
In this paper, we discuss a prototype application deployed at the U.S. National Science Foundation for assisting program directors in identifying reviewers for proposals. The appl...
Seth Hettich, Michael J. Pazzani