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GECCO
2006
Springer
206views Optimization» more  GECCO 2006»
15 years 10 months ago
Adaptive discretization for probabilistic model building genetic algorithms
This paper proposes an adaptive discretization method, called Split-on-Demand (SoD), to enable the probabilistic model building genetic algorithm (PMBGA) to solve optimization pro...
Chao-Hong Chen, Wei-Nan Liu, Ying-Ping Chen
ICS
2010
Tsinghua U.
16 years 4 months ago
Distribution-Specific Agnostic Boosting
We consider the problem of boosting the accuracy of weak learning algorithms in the agnostic learning framework of Haussler (1992) and Kearns et al. (1992). Known algorithms for t...
Vitaly Feldman
ICIP
2005
IEEE
16 years 8 months ago
Curve segmentation using directional information, relation to pattern detection
We propose an extension of the conformal (or geodesic) active contour framework in which the conformal factor depends not only on the position of the curve but also on the directi...
Eric Pichon, Allen Tannenbaum
CEC
2007
IEEE
16 years 1 months ago
Towards a generic control strategy for Evolutionary Algorithms: an adaptive fuzzy-learning approach
— This paper presents a new method to generalize strategies in order to control parameters of Evolutionary Algorithms (EAs). A learning process establishes the relationship betwe...
Jorge Maturana, Frédéric Saubion
GLOBECOM
2007
IEEE
16 years 1 months ago
Stochastic Channel Selection in Cognitive Radio Networks
— In this paper, we investigate the channel selection strategy for secondary users in cognitive radio networks. We claim that in order to avoid the costly channel switchings, a s...
Yang Song, Yuguang Fang, Yanchao Zhang