Test-Cost Sensitive Classification Based on Conditioned Loss Functions
ECML '07 Proceedings of the 18th European conference on Machine Learning
Information fusion for computer security: State of the art and open issues
Information Fusion
Ambient Intelligence --From Personal Assistance to Intelligent Megacities
Proceedings of the 2007 conference on Advances in Ambient Intelligence
Landmark detection from mobile life log using a modular Bayesian network model
Expert Systems with Applications: An International Journal
Dynamic view planning by effective particles for three-dimensional tracking
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Networked data fusion with packet losses and variable delays
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hierarchical control models for multimodal process modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A driver fatigue recognition model based on information fusion and dynamic Bayesian network
Information Sciences: an International Journal
Cost-sensitive classification with respect to waiting cost
Knowledge-Based Systems
Efficient sensor selection for active information fusion
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Qualitative test-cost sensitive classification
Pattern Recognition Letters
Optimized particles for 3-D tracking
ICIRA'10 Proceedings of the Third international conference on Intelligent robotics and applications - Volume Part I
An improved decision-making rule of Dempster-Shafer theory application on fault diagnosis system
International Journal of Computer Applications in Technology
| Hi-index | 0.00 |
Many information fusion applications are often characterized by a high degree of complexity because: 1) data are often acquired from sensors of different modalities and with different degrees of uncertainty; 2) decisions must be made efficiently; and 3) the world situation evolves over time. To address these issues, we propose an information fusion framework based on dynamic Bayesian networks to provide active, dynamic, purposive and sufficing information fusion in order to arrive at a reliable conclusion with reasonable time and limited resources. The proposed framework is suited to applications where the decision must be made efficiently from dynamically available information of diverse and disparate sources.