A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use of integral operator kernel functions, one can e ciently compute principal comp...
This paper addresses the problem of transductive learning of the kernel matrix from a probabilistic perspective. We define the kernel matrix as a Wishart process prior and construc...
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...
We discuss a simple sparse linear problem that is hard to learn with any algorithm that uses a linear combination of the training instances as its weight vector. The hardness holds...
Abstract. We tackle the problem of multi-class relational sequence learning using relevant patterns discovered from a set of labelled sequences. To deal with this problem, firstly...
Nicola Di Mauro, Teresa Maria Altomare Basile, Ste...