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AAAI
1997
15 years 7 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 3 months 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
16 years 12 days 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 4 months 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 6 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