This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop e...
Jie Cheng, Russell Greiner, Jonathan Kelly, David ...
In the context of spoken language interpretation, this paper introduces a stochastic approach to infer and compose semantic structures. Semantic frame structures are directly deri...
In this paper, we study the problem of social relational inference using visual concepts which serve as indicators of actors’ social interactions. While social network analysis ...
—The hidden knowledge in social networks data can be regarded as an important resource for criminal investigations which can help finding the structure and organization of a crim...
According to widely accepted guidelines for self-regulation, the capital requirements of a bank should relate to the level of risk with respect to three different categories. Amon...
Alessandro Antonucci, Alberto Piatti, Marco Zaffal...