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COLING
2010
14 years 6 months ago
Kernel Slicing: Scalable Online Training with Conjunctive Features
This paper proposes an efficient online method that trains a classifier with many conjunctive features. We employ kernel computation called kernel slicing, which explicitly consid...
Naoki Yoshinaga, Masaru Kitsuregawa
JMLR
2008
140views more  JMLR 2008»
14 years 11 months ago
Aggregation of SVM Classifiers Using Sobolev Spaces
This paper investigates statistical performances of Support Vector Machines (SVM) and considers the problem of adaptation to the margin parameter and to complexity. In particular ...
Sébastien Loustau
NIPS
1996
15 years 1 months ago
Combinations of Weak Classifiers
To obtain classification systems with both good generalizat`ion performance and efficiency in space and time, we propose a learning method based on combinations of weak classifiers...
Chuanyi Ji, Sheng Ma
BMCBI
2010
182views more  BMCBI 2010»
14 years 12 months ago
L2-norm multiple kernel learning and its application to biomedical data fusion
Background: This paper introduces the notion of optimizing different norms in the dual problem of support vector machines with multiple kernels. The selection of norms yields diff...
Shi Yu, Tillmann Falck, Anneleen Daemen, Lé...
PAA
2002
14 years 11 months ago
Bagging, Boosting and the Random Subspace Method for Linear Classifiers
: Recently bagging, boosting and the random subspace method have become popular combining techniques for improving weak classifiers. These techniques are designed for, and usually ...
Marina Skurichina, Robert P. W. Duin