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» Optimal feature selection for support vector machines
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JMLR
2006
124views more  JMLR 2006»
15 years 4 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
14 years 11 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
15 years 9 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»
14 years 7 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
15 years 10 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. ...