Sciweavers

AVSS
2009
IEEE
13 years 7 months ago
Bayesian Bio-inspired Model for Learning Interactive Trajectories
—Automatic understanding of human behavior is an important and challenging objective in several surveillance applications. One of the main problems of this task consists in accur...
Alessio Dore, Carlo S. Regazzoni
ECAI
2006
Springer
13 years 8 months ago
Smoothed Particle Filtering for Dynamic Bayesian Networks
Particle filtering (PF) for dynamic Bayesian networks (DBNs) with discrete-state spaces includes a resampling step which concentrates samples according to their relative weight in ...
Theodore Charitos
NN
1997
Springer
174views Neural Networks» more  NN 1997»
13 years 8 months ago
Learning Dynamic Bayesian Networks
Bayesian networks are directed acyclic graphs that represent dependencies between variables in a probabilistic model. Many time series models, including the hidden Markov models (H...
Zoubin Ghahramani
CDC
2009
IEEE
167views Control Systems» more  CDC 2009»
13 years 9 months ago
Factoring Dynamic Bayesian Networks based on structural observability
— Dynamic Bayesian Networks (DBNs) provide a systematic framework for robust online monitoring of dynamic systems. This paper presents an approach for increasing the efficiency ...
Indranil Roychoudhury, Gautam Biswas, Xenofon D. K...
GECCO
2003
Springer
182views Optimization» more  GECCO 2003»
13 years 9 months ago
Spatial Operators for Evolving Dynamic Bayesian Networks from Spatio-temporal Data
Learning Bayesian networks from data has been studied extensively in the evolutionary algorithm communities [Larranaga96, Wong99]. We have previously explored extending some of the...
Allan Tucker, Xiaohui Liu, David Garway-Heath
UM
2005
Springer
13 years 10 months ago
Data-Driven Refinement of a Probabilistic Model of User Affect
We present further developments in our work on using data from real users to build a probabilistic model of user affect based on Dynamic Bayesian Networks (DBNs) and designed to de...
Cristina Conati, Heather Maclaren
IROS
2007
IEEE
125views Robotics» more  IROS 2007»
13 years 10 months ago
Probabilistic inference for structured planning in robotics
Abstract— Real-world robotic environments are highly structured. The scalability of planning and reasoning methods to cope with complex problems in such environments crucially de...
Marc Toussaint, Christian Goerick
ICALT
2008
IEEE
13 years 11 months ago
Designing a Dynamic Bayesian Network for Modeling Students' Learning Styles
When using Learning Object Repositories, it is interesting to have mechanisms to select the more adequate objects for each student. For this kind of adaptation, it is important to...
Cristina Carmona, Gladys Castillo, Eva Millá...
AVSS
2008
IEEE
13 years 11 months ago
Person Tracking with Audio-Visual Cues Using the Iterative Decoding Framework
Tracking humans in an indoor environment is an essential part of surveillance systems. Vision based and microphone array based trackers have been extensively researched in the pas...
Shankar T. Shivappa, Mohan M. Trivedi, Bhaskar D. ...
ICML
2004
IEEE
14 years 5 months ago
Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data
In sequence modeling, we often wish to represent complex interaction between labels, such as when performing multiple, cascaded labeling tasks on the same sequence, or when longra...
Charles A. Sutton, Khashayar Rohanimanesh, Andrew ...