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TCBB
2010
176views more  TCBB 2010»
15 years 2 months ago
Feature Selection for Gene Expression Using Model-Based Entropy
—Gene expression data usually contain a large number of genes, but a small number of samples. Feature selection for gene expression data aims at finding a set of genes that best...
Shenghuo Zhu, Dingding Wang, Kai Yu, Tao Li, Yihon...
ICML
2007
IEEE
16 years 4 months ago
Kernelizing PLS, degrees of freedom, and efficient model selection
Kernelizing partial least squares (PLS), an algorithm which has been particularly popular in chemometrics, leads to kernel PLS which has several interesting properties, including ...
Mikio L. Braun, Nicole Krämer
IJAR
2010
97views more  IJAR 2010»
15 years 2 months ago
Parameter estimation and model selection for mixtures of truncated exponentials
Bayesian networks with mixtures of truncated exponentials (MTEs) support efficient inference algorithms and provide a flexible way of modeling hybrid domains (domains containing ...
Helge Langseth, Thomas D. Nielsen, Rafael Rum&iacu...
CORR
2007
Springer
90views Education» more  CORR 2007»
15 years 3 months ago
Mutual information for the selection of relevant variables in spectrometric nonlinear modelling
Data from spectrophotometers form vectors of a large number of exploitable variables. Building quantitative models using these variables most often requires using a smaller set of...
Fabrice Rossi, Amaury Lendasse, Damien Franç...
ICONIP
2004
15 years 5 months ago
Hybrid Feature Selection for Modeling Intrusion Detection Systems
Most of the current Intrusion Detection Systems (IDS) examine all data features to detect intrusion or misuse patterns. Some of the features may be redundant or contribute little (...
Srilatha Chebrolu, Ajith Abraham, Johnson P. Thoma...