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CVPR
2008
IEEE
14 years 9 months ago
Boosting adaptive linear weak classifiers for online learning and tracking
Online boosting methods have recently been used successfully for tracking, background subtraction etc. Conventional online boosting algorithms emphasize on interchanging new weak ...
Toufiq Parag, Fatih Porikli, Ahmed M. Elgammal
COLT
2008
Springer
13 years 9 months ago
Extracting Certainty from Uncertainty: Regret Bounded by Variation in Costs
Prediction from expert advice is a fundamental problem in machine learning. A major pillar of the field is the existence of learning algorithms whose average loss approaches that ...
Elad Hazan, Satyen Kale
CE
2004
88views more  CE 2004»
13 years 7 months ago
Beyond formal learning: Informal community eLearning
The goal of the study described in this paper was to gain an improved understanding of the social context of UK online centres and issues around the creation and exchange of knowl...
John Cook, Matt Smith
CORR
2011
Springer
171views Education» more  CORR 2011»
13 years 2 months ago
Parallel Online Learning
Online learning algorithms have impressive convergence properties when it comes to risk minimization and convex games on very large problems. However, they are inherently sequenti...
Daniel Hsu, Nikos Karampatziakis, John Langford, A...
IJAC
2008
63views more  IJAC 2008»
13 years 7 months ago
The Management Challenge Online: e-Learning in Practice
- The Management Challenge Online (TMCO) is a ten week online management course that begins for a cohort of 30 in the week immediately following their attendance at a residential t...
Anna Peachey, Sarah Walshe