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ICML
2004
IEEE
14 years 2 months ago
Gradient LASSO for feature selection
LASSO (Least Absolute Shrinkage and Selection Operator) is a useful tool to achieve the shrinkage and variable selection simultaneously. Since LASSO uses the L1 penalty, the optim...
Yongdai Kim, Jinseog Kim
CATA
2009
13 years 10 months ago
Nearest Shrunken Centroid as Feature Selection of Microarray Data
The nearest shrunken centroid classifier uses shrunken centroids as prototypes for each class and test samples are classified to belong to the class whose shrunken centroid is nea...
Myungsook Klassen, Nyunsu Kim
AAAI
2011
12 years 9 months ago
A Feasible Nonconvex Relaxation Approach to Feature Selection
Variable selection problems are typically addressed under a penalized optimization framework. Nonconvex penalties such as the minimax concave plus (MCP) and smoothly clipped absol...
Cuixia Gao, Naiyan Wang, Qi Yu, Zhihua Zhang
ICMCS
2005
IEEE
145views Multimedia» more  ICMCS 2005»
14 years 2 months ago
From Physiological Signals to Emotions: Implementing and Comparing Selected Methods for Feature Extraction and Classification
Little attention has been paid so far to physiological signals for emotion recognition compared to audio-visual emotion channels, such as facial expressions or speech. In this pap...
Johannes Wagner, Jonghwa Kim, Elisabeth Andr&eacut...
ESANN
2008
13 years 10 months ago
Classification of chestnuts with feature selection by noise resilient classifiers
In this paper we solve the problem of classifying chestnut plants according to their place of origin. We compare the results obtained by state of the art classifiers, among which,...
Elena Roglia, Rossella Cancelliere, Rosa Meo