A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
ContextPhone: A Prototyping Platform for Context-Aware Mobile Applications
IEEE Pervasive Computing
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
InSense: Interest-Based Life Logging
IEEE MultiMedia
Human computing and machine understanding of human behavior: a survey
Proceedings of the 8th international conference on Multimodal interfaces
Using the influence model to recognize functional roles in meetings
Proceedings of the 9th international conference on Multimodal interfaces
A quantitative analysis of the collective creativity in playing 20-questions games
Proceedings of the seventh ACM conference on Creativity and cognition
Human computing and machine understanding of human behavior: a survey
ICMI'06/IJCAI'07 Proceedings of the ICMI 2006 and IJCAI 2007 international conference on Artifical intelligence for human computing
| Hi-index | 0.00 |
A common problem of ubiquitous sensor-network computing is combining evidence between multiple agents or experts. We demonstrate that the latent structure influence model, our novel formulation for combining evidence from multiple dynamic classification processes ("experts"), can achieve greater accuracy, efficiency, and robustness to data corruption than standard methods such as HMMs. It accomplishes this by simultaneously modeling the structure of interaction and the latent states.