Abstract. In this paper we present a novel and general framework based on concepts of relational algebra for kernel-based learning over relational schema. We exploit the notion of ...
Adam Woznica, Alexandros Kalousis, Melanie Hilario
We consider the problem of specifying data structures with complex sharing in a manner that is both declarative and results in provably correct code. In our approach, abstract data...
Peter Hawkins, Alex Aiken, Kathleen Fisher, Martin...
Abstract. Gaussian processes have successfully been used to learn preferences among entities as they provide nonparametric Bayesian approaches for model selection and probabilistic...
Abstract. The application of kernel methods to link analysis is explored. We argue that a family of kernels on graphs provides a unified perspective on the three measures proposed ...
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...