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NIPS
2001
13 years 9 months ago
Efficiency versus Convergence of Boolean Kernels for On-Line Learning Algorithms
The paper studies machine learning problems where each example is described using a set of Boolean features and where hypotheses are represented by linear threshold elements. One ...
Roni Khardon, Dan Roth, Rocco A. Servedio
CORR
2011
Springer
198views Education» more  CORR 2011»
12 years 11 months ago
Decentralized Online Learning Algorithms for Opportunistic Spectrum Access
—The fundamental problem of multiple secondary users contending for opportunistic spectrum access over multiple channels in cognitive radio networks has been formulated recently ...
Yi Gai, Bhaskar Krishnamachari
COLT
2006
Springer
13 years 11 months ago
A Randomized Online Learning Algorithm for Better Variance Control
We propose a sequential randomized algorithm, which at each step concentrates on functions having both low risk and low variance with respect to the previous step prediction functi...
Jean-Yves Audibert
GECCO
2007
Springer
137views Optimization» more  GECCO 2007»
14 years 1 months ago
Learning and anticipation in online dynamic optimization with evolutionary algorithms: the stochastic case
The focus of this paper is on how to design evolutionary algorithms (EAs) for solving stochastic dynamic optimization problems online, i.e. as time goes by. For a proper design, t...
Peter A. N. Bosman, Han La Poutré
JMLR
2011
137views more  JMLR 2011»
13 years 2 months ago
Online Learning in Case of Unbounded Losses Using Follow the Perturbed Leader Algorithm
In this paper the sequential prediction problem with expert advice is considered for the case where losses of experts suffered at each step cannot be bounded in advance. We presen...
Vladimir V. V'yugin