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IJON
2011
158views more  IJON 2011»
13 years 2 months ago
Maximal Discrepancy for Support Vector Machines
Several theoretical methods have been developed in the past years to evaluate the generalization ability of a classifier: they provide extremely useful insights on the learning ph...
Davide Anguita, Alessandro Ghio, Sandro Ridella
PRL
2008
124views more  PRL 2008»
13 years 7 months ago
Matrix-pattern-oriented least squares support vector classifier with AdaBoost
: Matrix-pattern-oriented Least Squares Support Vector Classifier (MatLSSVC) can directly classify matrix patterns and has a superior classification performance than its vector ver...
Zhe Wang, Songcan Chen
ICPR
2008
IEEE
14 years 8 months ago
Efficient background modeling through incremental Support Vector Data Description
Background modeling is an essential and important part of many high-level video processing applications. Recently, the Support Vector Data Description (SVDD) has been introduced f...
Alireza Tavakkoli, Mircea Nicolescu, George Bebis,...
EUROMICRO
2003
IEEE
14 years 28 days ago
Color Texture Recognition in Video Sequences using Wavelet Covariance Features and Support Vector Machines
This paper pertains to the recognition of textural regions for color video analysis. The proposed scheme uses the covariance of 2nd -order statistics on the wavelet domain, betwee...
Dimitrios K. Iakovidis, Dimitrios E. Maroulis, S. ...
NIPS
2004
13 years 9 months ago
Parallel Support Vector Machines: The Cascade SVM
We describe an algorithm for support vector machines (SVM) that can be parallelized efficiently and scales to very large problems with hundreds of thousands of training vectors. I...
Hans Peter Graf, Eric Cosatto, Léon Bottou,...