Graph-based semi-supervised learning has gained considerable
interests in the past several years thanks to its effectiveness
in combining labeled and unlabeled data through
labe...
We consider the problem of dealing with irrelevant votes when a multi-case classifier is built from an ensemble of binary classifiers. We show how run-off elections can be used to...
We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised...
The Blob Code is a bijective tree code that represents each tree on n labelled vertices as a string of n − 2 vertex labels. In recent years, several researchers have deployed th...
In this talk we will present a new approach to deal with attributed graphs and attributed graph transformation. The approach is based on working with what we call symbolic graphs, ...