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ICPR
2010
IEEE
13 years 11 months ago
Fast Training of Object Detection Using Stochastic Gradient Descent
Training datasets for object detection problems are typically very large and Support Vector Machine (SVM) implementations are computationally complex. As opposed to these complex ...
Rob Wijnhoven, Peter H. N. De With
GECCO
2009
Springer
14 years 3 months ago
On the scalability of XCS(F)
Many successful applications have proven the potential of Learning Classifier Systems and the XCS classifier system in particular in datamining, reinforcement learning, and func...
Patrick O. Stalph, Martin V. Butz, David E. Goldbe...
GECCO
2006
Springer
179views Optimization» more  GECCO 2006»
14 years 17 days ago
Parisian evolution with honeybees for three-dimensional reconstruction
This paper introduces a novel analogy with the way in which honeybee colonies operate in order to solve the problem of sparse and quasi dense reconstruction. To successfully solve...
Gustavo Olague, Cesar Puente
IIS
2000
13 years 10 months ago
Speeding Up Evolution through Learning: LEM
This paper reports briefly on the development of a new approach to evolutionary computation, called the Learnable Evolution Model or LEM. In contrast to conventional Darwinian-typ...
Ryszard S. Michalski, Guido Cervone, Kenneth A. Ka...
NIPS
1997
13 years 10 months ago
Learning to Schedule Straight-Line Code
Program execution speed on modern computers is sensitive, by a factor of two or more, to the order in which instructions are presented to the processor. To realize potential execu...
J. Eliot B. Moss, Paul E. Utgoff, John Cavazos, Do...