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ISCIS
2003
Springer
15 years 5 months ago
A New Continuous Action-Set Learning Automaton for Function Optimization
In this paper, we study an adaptive random search method based on continuous action-set learning automaton for solving stochastic optimization problems in which only the noisecorr...
Hamid Beigy, Mohammad Reza Meybodi
105
Voted
MP
2008
126views more  MP 2008»
15 years 20 days ago
Geometry of interpolation sets in derivative free optimization
We consider derivative free methods based on sampling approaches for nonlinear optimization problems where derivatives of the objective function are not available and cannot be dir...
Andrew R. Conn, Katya Scheinberg, Luís N. V...
100
Voted
ICCS
2007
Springer
15 years 4 months ago
Efficient Implementation of an Optimal Interpolator for Large Spatial Data Sets
Abstract. Interpolating scattered data points is a problem of wide ranging interest. One of the most popular interpolation methods in geostatistics is ordinary kriging. The price f...
Nargess Memarsadeghi, David M. Mount
JORS
2010
87views more  JORS 2010»
14 years 11 months ago
The challenge of optimizing expensive black boxes: a scatter search/rough set theory approach
  There is renewed interest in the development of effective and efficient methods for optimizing models  of  which  the  optimizer  has  no  structural  know...
Manuel Laguna, Julián Molina Luque, Fatima ...
98
Voted
CVPR
2010
IEEE
15 years 9 months ago
Optimizing One-Shot Recognition with Micro-Set Learning
For object category recognition to scale beyond a small number of classes, it is important that algorithms be able to learn from a small amount of labeled data per additional clas...
Kevin Tang, Marshall Tappen, Rahul Sukthankar, Chr...