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» Parallelizing Feature Selection
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ICPR
2008
IEEE
16 years 28 days ago
Adaptive asymmetrical SVM and genetic algorithms based iris recognition
We propose Genetic Algorithms to improve the feature subset selection by combining the valuable outcomes from multiple feature selection methods. This paper also motivates the use...
Kaushik Roy 0002, Prabir Bhattacharya
IDEAL
2005
Springer
16 years 14 hour ago
A Comparative Study of Two Novel Predictor Set Scoring Methods
Due to the large number of genes measured in a typical microarray dataset, feature selection plays an essential role in tumor classification. In turn, relevance and redundancy are ...
Chia Huey Ooi, Madhu Chetty
AUSAI
2007
Springer
15 years 10 months ago
Building Classification Models from Microarray Data with Tree-Based Classification Algorithms
Building classification models plays an important role in DNA mircroarray data analyses. An essential feature of DNA microarray data sets is that the number of input variables (gen...
Peter J. Tan, David L. Dowe, Trevor I. Dix
PDCAT
2009
Springer
16 years 1 months ago
Supporting Partial Ordering with the Parallel Iterator
With the advent of multi-core processors, desktop application developers must finally face parallel computing and its challenges. A large portion of the computational load in a p...
Nasser Giacaman, Oliver Sinnen
AFP
2008
Springer
236views Formal Methods» more  AFP 2008»
16 years 26 days ago
A Tutorial on Parallel and Concurrent Programming in Haskell
This practical tutorial introduces the features available in Haskell for writing parallel and concurrent programs. We first describe how to write semi-explicit parallel programs b...
Simon L. Peyton Jones, Satnam Singh