We study the problem of learning using combinations of machines. In particular we present new theoretical bounds on the generalization performance of voting ensembles of kernel ma...
Abstract. This work proposes a family of language-independent semantic kernel functions defined for individuals in an ontology. This allows exploiting wellfounded kernel methods fo...
In this chapter, we describe a view of statistical learning in the inductive logic programming setting based on kernel methods. The relational representation of data and background...
The problem of learning a transduction, that is a string-to-string mapping, is a common problem arising in natural language processing and computational biology. Previous methods ...
Building semantics concepts into the learning tools and utilities will open the road for the next generation of the Learning Content Management System (LCMS). We propose here to n...