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» On learning algorithm selection for classification
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ICML
2008
IEEE
16 years 7 months ago
ManifoldBoost: stagewise function approximation for fully-, semi- and un-supervised learning
We introduce a boosting framework to solve a classification problem with added manifold and ambient regularization costs. It allows for a natural extension of boosting into both s...
Nicolas Loeff, David A. Forsyth, Deepak Ramachandr...
171
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COLT
2006
Springer
15 years 10 months ago
Online Variance Minimization
We design algorithms for two online variance minimization problems. Specifically, in every trial t our algorithms get a covariance matrix Ct and try to select a parameter vector wt...
Manfred K. Warmuth, Dima Kuzmin
ALDT
2011
Springer
262views Algorithms» more  ALDT 2011»
14 years 6 months ago
Learning Complex Concepts Using Crowdsourcing: A Bayesian Approach
Abstract. We develop a Bayesian approach to concept learning for crowdsourcing applications. A probabilistic belief over possible concept definitions is maintained and updated acc...
Paolo Viappiani, Sandra Zilles, Howard J. Hamilton...
ICML
2008
IEEE
16 years 7 months ago
Reinforcement learning in the presence of rare events
We consider the task of reinforcement learning in an environment in which rare significant events occur independently of the actions selected by the controlling agent. If these ev...
Jordan Frank, Shie Mannor, Doina Precup
JAIR
2006
111views more  JAIR 2006»
15 years 6 months ago
Learning in Real-Time Search: A Unifying Framework
Real-time search methods are suited for tasks in which the agent is interacting with an initially unknown environment in real time. In such simultaneous planning and learning prob...
Vadim Bulitko, Greg Lee