In this paper, supervised nonparametric information theoretic classification (ITC) is introduced. Its principle relies on the likelihood of a data sample of transmitting its class...
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...
Support Vector Machine (SVM) has been widely studied and shown success in many application fields. However, the performance of SVM drops significantly when it is applied to the pr...
We show how the regularizer of Transductive Support Vector Machines (TSVM) can be trained by stochastic gradient descent for linear models and multi-layer architectures. The resul...
Michael Karlen, Jason Weston, Ayse Erkan, Ronan Co...
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...