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Erschienen in:
2007 | OriginalPaper | Buchkapitel
Predictive Learning in Sensor Networks
verfasst von : João Gama, Rasmus Ulslev Pedersen
Erschienen in: Learning from Data Streams
Verlag: Springer Berlin Heidelberg
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Sensor networks act in dynamic environments with distributed sources of continuous data and computing with resource constraints. Learning in these environments is faced with new challenges: the need to continuously maintain a decision model consistent with the most recent data. Desirable properties of learning algorithms include: the ability to maintain an any time model; the ability to modify the decision model whenever new information is available; the ability to forget outdated information; and the ability to detect and react to changes in the underlying process generating data, monitoring the learning process and managing the trade-off between the cost of updating a model and the benefits in performance gains. In this chapter we illustrate these ideas in two learning scenarios—centralized and distributed—and present illustrative algorithms for these contexts.