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...
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...
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 ...
We study the problem of private classification using kernel methods. More specifically, we propose private protocols implementing the Kernel Adatron and Kernel Perceptron 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 ...