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» Making generative classifiers robust to selection bias
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FGR
2004
IEEE
200views Biometrics» more  FGR 2004»
13 years 9 months ago
Using Random Subspace to Combine Multiple Features for Face Recognition
LDA is a popular subspace based face recognition approach. However, it often suffers from the small sample size problem. When dealing with the high dimensional face data, the LDA ...
Xiaogang Wang, Xiaoou Tang
GECCO
2006
Springer
195views Optimization» more  GECCO 2006»
13 years 9 months ago
Studying XCS/BOA learning in Boolean functions: structure encoding and random Boolean functions
Recently, studies with the XCS classifier system on Boolean functions have shown that in certain types of functions simple crossover operators can lead to disruption and, conseque...
Martin V. Butz, Martin Pelikan
BMCBI
2008
130views more  BMCBI 2008»
13 years 5 months ago
A novel series of compositionally biased substitution matrices for comparing Plasmodium proteins
Background: The most common substitution matrices currently used (BLOSUM and PAM) are based on protein sequences with average amino acid distributions, thus they do not represent ...
Kevin Brick, Elisabetta Pizzi
GECCO
2006
Springer
140views Optimization» more  GECCO 2006»
13 years 9 months ago
A representational ecology for learning classifier systems
The representation used by a learning algorithm introduces a bias which is more or less well-suited to any given learning problem. It is well known that, across all possible probl...
James A. R. Marshall, Tim Kovacs
WCE
2007
13 years 6 months ago
Gene Selection for Tumor Classification Using Microarray Gene Expression Data
– In this paper we perform a t-test for significant gene expression analysis in different dimensions based on molecular profiles from microarray data, and compare several computa...
Krishna Yendrapalli, Ram B. Basnet, Srinivas Mukka...