Modeling influence between experts

Abstract

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. © 2007 Springer-Verlag Berlin Heidelberg.