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BMCBI
2008
88views more  BMCBI 2008»
15 years 7 months ago
Use of machine learning algorithms to classify binary protein sequences as highly-designable or poorly-designable
Background: By using a standard Support Vector Machine (SVM) with a Sequential Minimal Optimization (SMO) method of training, Na
Myron Peto, Andrzej Kloczkowski, Vasant Honavar, R...
JPDC
2006
133views more  JPDC 2006»
15 years 6 months ago
Grid computing for parallel bioinspired algorithms
This paper focuses on solving large size combinatorial optimization problems using a Grid-enabled framework called ParadisEO
Nouredine Melab, Sébastien Cahon, El-Ghazal...
GECCO
2005
Springer
177views Optimization» more  GECCO 2005»
16 years 10 days ago
Evolving next generation signal compression and reconstruction transforms via genetic algorithms
Ongoing research has established a new methodology for using genetic algorithms [2] to evolve forward and inverse transforms that significantly reduce quantization error in recons...
Frank W. Moore, Patrick Marshall
EUSFLAT
2003
121views Fuzzy Logic» more  EUSFLAT 2003»
15 years 8 months ago
An adaptive learning algorithm for a neo fuzzy neuron
In the paper, a new optimal learning algorithm for a neo-fuzzy neuron (NFN) is proposed. The algorithm is characteristic in that it provides online tuning of not only the synaptic...
Yevgeniy Bodyanskiy, Illya Kokshenev, Vitaliy Kolo...
ALGORITHMICA
2010
112views more  ALGORITHMICA 2010»
15 years 7 months ago
Approximation Algorithms for Treewidth
Abstract. This paper presents algorithms whose input is an undirected graph, and whose output is a tree decomposition of width that approximates the optimal, the treewidth of that ...
Eyal Amir