Boosted Audio-Visual HMM for Speech Reading
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Multi-sensory and Multi-modal Fusion for Sentient Computing
International Journal of Computer Vision
Boosted Bayesian network classifiers
Machine Learning
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
The Infinite Latent Events Model
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Empirical analysis of an on-line adaptive system using a mixture of Bayesian networks
Information Sciences: an International Journal
Exhaustive simulation of consecutive mental states of human agents
Knowledge-Based Systems
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Bayesian networks are an attractive modeling tool for human sensing, as they combine an intuitive graphical representation with efficient algorithms for inference and learning. Earlier work has demonstrated that boosted parameter learning could be used to improve the performance of Bayesian network classifiers for complex multi-modal inference problems such as speaker detection. In speaker detection, the goal is to use video and audio cues to infer when a person is speaking to a user interface. In this paper we introduce a new boosted structure learning algorithm based on AdaBoost. Given labeled data, our algorithm modifiesboth the network structure and parameters so as to improve classification accuracy. We compare its performance to both standard structure learning and boosted parameterlearning on a fixed structure. We present results for speaker detection and for the UCI "chess" dataset.