We present a novel mixed-state dynamic Bayesian network (DBN) framework for modeling and classifying timeseries data such as object trajectories. A hidden Markov model (HMM) of di...
Vladimir Pavlovic, Brendan J. Frey, Thomas S. Huan...
In this work we present a novel multi-modal mixed-state dynamic Bayesian network (DBN) for robust meeting event classification. The model uses information from lapel microphones,...
In this paper, we focus on lexical semantics, a key issue in Natural Language Processing (NLP) that tends to converge with conceptual Knowledge Representation (KR) and ontologies....
In this paper we address the problem of discretization in the context of learning Bayesian networks (BNs) from data containing both continuous and discrete variables. We describe ...
Abstract--We devise an optimization framework for generalized proportional fairness (GPF) under different time scales for amplify-and-forward (AF) relay networks. In GPF scheduling...
Alireza Sharifian, Petar Djukic, Halim Yanikomerog...