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JMLR
2008
104views more  JMLR 2008»
14 years 10 months ago
Learning Reliable Classifiers From Small or Incomplete Data Sets: The Naive Credal Classifier 2
In this paper, the naive credal classifier, which is a set-valued counterpart of naive Bayes, is extended to a general and flexible treatment of incomplete data, yielding a new cl...
Giorgio Corani, Marco Zaffalon
SDM
2009
SIAM
219views Data Mining» more  SDM 2009»
15 years 8 months ago
A Hybrid Data Mining Metaheuristic for the p-Median Problem.
Metaheuristics represent an important class of techniques to solve, approximately, hard combinatorial optimization problems for which the use of exact methods is impractical. In t...
Alexandre Plastino, Erick R. Fonseca, Richard Fuch...
FUZZIEEE
2007
IEEE
15 years 5 months ago
Learning Fuzzy Linguistic Models from Low Quality Data by Genetic Algorithms
— Incremental rule base learning techniques can be used to learn models and classifiers from interval or fuzzyvalued data. These algorithms are efficient when the observation e...
Luciano Sánchez, José Otero
BMCBI
2007
173views more  BMCBI 2007»
14 years 10 months ago
Recursive Cluster Elimination (RCE) for classification and feature selection from gene expression data
Background: Classification studies using gene expression datasets are usually based on small numbers of samples and tens of thousands of genes. The selection of those genes that a...
Malik Yousef, Segun Jung, Louise C. Showe, Michael...
ISCI
2008
165views more  ISCI 2008»
14 years 10 months ago
Support vector regression from simulation data and few experimental samples
This paper considers nonlinear modeling based on a limited amount of experimental data and a simulator built from prior knowledge. The problem of how to best incorporate the data ...
Gérard Bloch, Fabien Lauer, Guillaume Colin...