Teaching Contexts to Applications
نویسنده
چکیده
Enhancing applications by adding one or more sensors is not new. Incorporating machinelearning techniques to fuse the data from the sensors into a high-level context description is less obvious. This paper describes an architecture that combines a hierarchy of self-organizing networks and a Markov chain to enable on-line context recognition. Depending on both user and application, the user can teach a context description to the system whenever he or she likes to, as long as the behavior of the sensors is different enough. Finally, consequences and complications of this new approach are discussed.
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