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JCB
2002
70views more  JCB 2002»
13 years 8 months ago
Strong Feature Sets from Small Samples
For small samples, classi er design algorithms typically suffer from over tting. Given a set of features, a classi er must be designed and its error estimated. For small samples, ...
Seungchan Kim, Edward R. Dougherty, Junior Barrera...
PR
2007
148views more  PR 2007»
13 years 8 months ago
Learning the best subset of local features for face recognition
We propose a novel, local feature-based face representation method based on twostage subset selection where the first stage finds the informative regions and the second stage ...
Berk Gökberk, M. Okan Irfanoglu, Lale Akarun,...
JMLR
2010
133views more  JMLR 2010»
13 years 3 months ago
Exclusive Lasso for Multi-task Feature Selection
We propose a novel group regularization which we call exclusive lasso. Unlike the group lasso regularizer that assumes covarying variables in groups, the proposed exclusive lasso ...
Yang Zhou, Rong Jin, Steven C. H. Hoi
ICASSP
2010
IEEE
13 years 7 months ago
Feature extraction for robust speech recognition based on maximizing the sharpness of the power distribution and on power floori
This paper presents a new robust feature extraction algorithm based on a modified approach to power bias subtraction combined with applying a threshold to the power spectral dens...
Chanwoo Kim, Richard M. Stern
SPEECH
2011
13 years 3 months ago
Discrimination of speech from nonspeeech in broadcast news based on modulation frequency features
We describe a content based speech discrimination algorithm in broadcast news based on the time-varying information provided by the modulation spectrum. Due to the varying degrees...
Maria E. Markaki, Yannis Stylianou