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» Robust feature induction for support vector machines
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SMC
2007
IEEE
113views Control Systems» more  SMC 2007»
14 years 4 months ago
Robust multi-modal biometric fusion via multiple SVMs
—Existing learning-based multi-modal biometric fusion techniques typically employ a single static Support Vector Machine (SVM). This type of fusion improves the accuracy of biome...
Sabra Dinerstein, Jonathan Dinerstein, Dan Ventura
ICML
2010
IEEE
13 years 11 months ago
Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets
A sparse representation of Support Vector Machines (SVMs) with respect to input features is desirable for many applications. In this paper, by introducing a 0-1 control variable t...
Mingkui Tan, Li Wang, Ivor W. Tsang
ICML
2007
IEEE
14 years 11 months ago
Minimum reference set based feature selection for small sample classifications
We address feature selection problems for classification of small samples and high dimensionality. A practical example is microarray-based cancer classification problems, where sa...
Xue-wen Chen, Jong Cheol Jeong
ICMLA
2009
13 years 8 months ago
Feature Extraction and Classification of EEG Signals for Rapid P300 Mind Spelling
The Mind Speller is a Brain-Computer Interface which enables subjects to spell text on a computer screen by detecting P300 Event-Related Potentials in their electroencephalograms....
Adrien Combaz, Nikolay V. Manyakov, Nikolay Chumer...
ICASSP
2011
IEEE
13 years 1 months ago
SVM feature selection for multidimensional EEG data
In many machine learning applications, like Brain - Computer Interfaces (BCI), only high-dimensional noisy data are available rendering the discrimination task non-trivial. In thi...
Nisrine Jrad, Ronald Phlypo, Marco Congedo