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NCA
2008
IEEE
13 years 4 months ago
Polynomial kernel adaptation and extensions to the SVM classifier learning
Three extensions to the Kernel-AdaTron training algorithm for Support Vector Machine classifier learning are presented. These extensions allow the trained classifier to adhere more...
Ramy Saad, Saman K. Halgamuge, Jason Li
ICML
2005
IEEE
14 years 5 months ago
Large scale genomic sequence SVM classifiers
In genomic sequence analysis tasks like splice site recognition or promoter identification, large amounts of training sequences are available, and indeed needed to achieve suffici...
Bernhard Schölkopf, Gunnar Rätsch, S&oum...
JMLR
2008
140views more  JMLR 2008»
13 years 4 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
KDD
2006
ACM
181views Data Mining» more  KDD 2006»
14 years 5 months ago
Cryptographically private support vector machines
We study the problem of private classification using kernel methods. More specifically, we propose private protocols implementing the Kernel Adatron and Kernel Perceptron learning ...
Helger Lipmaa, Sven Laur, Taneli Mielikäinen
PAMI
2012
11 years 7 months ago
Domain Transfer Multiple Kernel Learning
—Cross-domain learning methods have shown promising results by leveraging labeled patterns from the auxiliary domain to learn a robust classifier for the target domain which has ...
Lixin Duan, Ivor W. Tsang, Dong Xu