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» Optimal feature selection for support vector machines
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JMLR
2006
124views more  JMLR 2006»
13 years 7 months ago
A Direct Method for Building Sparse Kernel Learning Algorithms
Many kernel learning algorithms, including support vector machines, result in a kernel machine, such as a kernel classifier, whose key component is a weight vector in a feature sp...
Mingrui Wu, Bernhard Schölkopf, Gökhan H...
BMCBI
2011
13 years 2 months ago
NClassG+: A classifier for non-classically secreted Gram-positive bacterial proteins
Background: Most predictive methods currently available for the identification of protein secretion mechanisms have focused on classically secreted proteins. In fact, only two met...
Daniel Restrepo-Montoya, Camilo Pino, Luis F. Ni&n...
MLMI
2005
Springer
14 years 1 months ago
Dominance Detection in Meetings Using Easily Obtainable Features
We show that, using a Support Vector Machine classifier, it is possible to determine with a 75% success rate who dominated a particular meeting on the basis of a few basic feature...
Rutger Rienks, Dirk Heylen
FGR
2011
IEEE
268views Biometrics» more  FGR 2011»
12 years 11 months ago
Emotion recognition using PHOG and LPQ features
— We propose a method for automatic emotion recognition as part of the FERA 2011 competition [1] . The system extracts pyramid of histogram of gradients (PHOG) and local phase qu...
Abhinav Dhall, Akshay Asthana, Roland Goecke, Tom ...
IMSCCS
2007
IEEE
14 years 2 months ago
Asymmetric Bagging and Feature Selection for Activities Prediction of Drug Molecules
Background: Activities of drug molecules can be predicted by QSAR (quantitative structure activity relationship) models, which overcomes the disadvantages of high cost and long cy...
Guo-Zheng Li, Hao-Hua Meng, Mary Qu Yang, Jack Y. ...