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CIBCB
2006
IEEE
14 years 1 months ago
A Model-Free Greedy Gene Selection for Microarray Sample Class Prediction
— Microarray data analysis is notoriously challenging as it involves a huge number of genes compared to only a limited number of samples. Gene selection, to detect the most signi...
Yi Shi, Zhipeng Cai, Lizhe Xu, Wei Ren, Randy Goeb...
WSC
1997
13 years 9 months ago
Bayesian Analysis for Simulation Input and Output
The paper summarizes some important results at the intersection of the fields of Bayesian statistics and stochastic simulation. Two statistical analysis issues for stochastic sim...
Stephen E. Chick
TEC
2010
191views more  TEC 2010»
13 years 2 months ago
Particle Swarm Optimization Aided Orthogonal Forward Regression for Unified Data Modeling
We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density func...
Sheng Chen, Xia Hong, Chris J. Harris
CVPR
2005
IEEE
14 years 9 months ago
Multilinear Independent Components Analysis
Independent Components Analysis (ICA) maximizes the statistical independence of the representational components of a training image ensemble, but it cannot distinguish between the...
M. Alex O. Vasilescu, Demetri Terzopoulos
BMCBI
2004
158views more  BMCBI 2004»
13 years 7 months ago
A novel Mixture Model Method for identification of differentially expressed genes from DNA microarray data
Background: The main goal in analyzing microarray data is to determine the genes that are differentially expressed across two types of tissue samples or samples obtained under two...
Kayvan Najarian, Maryam Zaheri, Ali Ajdari Rad, Si...