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BMCBI
2008
88views more  BMCBI 2008»
13 years 10 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...
FOCM
2006
65views more  FOCM 2006»
13 years 10 months ago
Approximation Methods for Supervised Learning
Let be an unknown Borel measure defined on the space Z := X
Ronald A. DeVore, Gerard Kerkyacharian, Dominique ...
GECCO
2006
Springer
130views Optimization» more  GECCO 2006»
14 years 1 months ago
Ensemble selection for evolutionary learning using information theory and price's theorem
This paper presents an information theoretic perspective on design and analysis of evolutionary algorithms. Indicators of solution quality are developed and applied not only to in...
Stuart W. Card, Chilukuri K. Mohan
ICPR
2008
IEEE
14 years 4 months ago
Improving Bayesian Network parameter learning using constraints
This paper describes a new approach to unify constraints on parameters with training data to perform parameter estimation in Bayesian networks of known structure. The method is ge...
Cassio Polpo de Campos, Qiang Ji
ADBIS
2005
Springer
100views Database» more  ADBIS 2005»
14 years 3 months ago
Evolutionary Learning of Boolean Queries by Genetic Programming
Abstract. The performance of an information retrieval system is usually measured in terms of two different criteria, precision and recall. This way, the optimization of any of its...
Suhail S. J. Owais, Pavel Krömer, Václ...