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.
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Dong, W., Pentland, A. (2007). Modeling Influence Between Experts. In: Huang, T.S., Nijholt, A., Pantic, M., Pentland, A. (eds) Artifical Intelligence for Human Computing. Lecture Notes in Computer Science(), vol 4451. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72348-6_9
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DOI: https://doi.org/10.1007/978-3-540-72348-6_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72346-2
Online ISBN: 978-3-540-72348-6
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