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» TRUST-TECH based Methods for Optimization and Learning
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IJON
2006
131views more  IJON 2006»
15 years 21 days ago
Optimizing blind source separation with guided genetic algorithms
This paper proposes a novel method for blindly separating unobservable independent component (IC) signals based on the use of a genetic algorithm. It is intended for its applicati...
J. M. Górriz, Carlos García Puntonet...
ESWS
2009
Springer
15 years 7 months ago
Improving Ontology Matching Using Meta-level Learning
Despite serious research efforts, automatic ontology matching still suffers from severe problems with respect to the quality of matching results. Existing matching systems trade-of...
Kai Eckert, Christian Meilicke, Heiner Stuckenschm...
GECCO
2008
Springer
155views Optimization» more  GECCO 2008»
15 years 1 months ago
Towards memoryless model building
Probabilistic model building methods can render difficult problems feasible by identifying and exploiting dependencies. They build a probabilistic model from the statistical prope...
David Iclanzan, Dumitru Dumitrescu
HVEI
2010
14 years 10 months ago
No-reference image quality assessment based on localized gradient statistics: application to JPEG and JPEG2000
This paper presents a novel system that employs an adaptive neural network for the no-reference assessment of perceived quality of JPEG/JPEG2000 coded images. The adaptive neural ...
Hantao Liu, Judith Redi, Hani Alers, Rodolfo Zunin...
CEC
2008
IEEE
15 years 7 months ago
The simplest evolution/learning hybrid: LEM with KNN
Abstract— The Learnable Evolution Model (LEM) was introduced by Michalski in 2000, and involves interleaved bouts of evolution and learning. Here we investigate LEM in (we think)...
Guleng Sheri, David W. Corne