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140
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ICPR
2002
IEEE
16 years 3 months ago
Boosting and Structure Learning in Dynamic Bayesian Networks for Audio-Visual Speaker Detection
Bayesian networks are an attractive modeling tool for human sensing, as they combine an intuitive graphical representation with ef?cient algorithms for inference and learning. Ear...
Tanzeem Choudhury, James M. Rehg, Vladimir Pavlovi...
124
Voted
VTC
2010
IEEE
180views Communications» more  VTC 2010»
15 years 27 days ago
Dynamic Channel Reservation to Enhance Channel Access by Exploiting Structure of Vehicular Networks
Abstract—VANET protocols need to exploit the special structure of vehicular networks. This structure includes the onedimensional nature of roads, the structure of lanes, the grou...
Ray K. Lam, P. R. Kumar
99
Voted
ICALT
2009
IEEE
15 years 9 months ago
Korean Word Associations: The Linked Structures for Language Learning
This paper reports on Korean Word Associations (KorWA) which were collected to construct a semantic network for Korean language. An approach of graph representation and network an...
Jaeyoung Jung, Nobuyasu Makoshi, Hiroyuki Akama
ISLPED
1995
ACM
125views Hardware» more  ISLPED 1995»
15 years 6 months ago
Transforming set data types to power optimal data structures
In this paper we present a novel approach to model the search space for optimal set data types in network component realisations. The main objective is to arrive at power efficie...
Sven Wuytack, Francky Catthoor, Hugo De Man
GECCO
2009
Springer
159views Optimization» more  GECCO 2009»
15 years 7 months ago
Bayesian network structure learning using cooperative coevolution
We propose a cooperative-coevolution – Parisian trend – algorithm, IMPEA (Independence Model based Parisian EA), to the problem of Bayesian networks structure estimation. It i...
Olivier Barrière, Evelyne Lutton, Pierre-He...