Energy-based learning (EBL) is a general framework to describe supervised and unsupervised training methods for probabilistic and non-probabilistic factor graphs. An energy-based ...
Existing density-based data stream clustering algorithms use a two-phase scheme approach consisting of an online phase, in which raw data is processed to gather summary statistics...
Agostino Forestiero, Clara Pizzuti, Giandomenico S...
1 Many applications require integrated access to multiple distributed, autonomous, and often semantically disparate data. Hence there is a need for bridging the semantic gap betwe...
Evolutionary computation methods have been used to solve several optimization and learning problems. This paper describes an application of evolutionary computation methods to con...
In this paper, we present a novel framework for machine learning-based cross-media knowledge extraction. The framework is specifically designed to handle documents composed of th...
A serious threat to user privacy in new mobile and web2.0 applications stems from ‘social inferences’. These unwanted inferences are related to the users’ identity, current ...
In this paper an original dynamic partition of formulae in Conjunctive Normal Form (CNF) is presented. It is based on the autarky concept first introduced by Monien and Speckenme...
In this paper a new learning scheme for SAT is proposed. The originality of our approach arises from its ability to achieve clause learning even if no conflict occurs. This clear...
Abstract—In distributed environments, access control decisions depend on statements of multiple agents rather than only one central trusted party. However, existing policy langua...