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- W2611804526 abstract "In this chapter, we analyze nonlinear filtering problems in distributed environments, e.g., sensor networks or peer-to-peer protocols. In these scenarios, the agents in the environment receive measurements in a streaming fashion, and they are required to estimate a common (nonlinear) model by alternating local computations and communications with their neighbors. We focus on the important distinction between single-task problems, where the underlying model is common to all agents, and multitask problems, where each agent might converge to a different model due to, e.g., spatial dependencies. Currently, most of the literature on distributed learning in the nonlinear case has focused on the single-task case, which may be a strong limitation in real-world scenarios. After introducing the problem and reviewing the existing approaches, we describe a simple kernel-based algorithm tailored for the multitask case. We evaluate the proposal on a simulated benchmark task, and we conclude by detailing currently open problems and lines of research." @default.
- W2611804526 created "2017-05-12" @default.
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- W2611804526 date "2018-01-01" @default.
- W2611804526 modified "2023-09-25" @default.
- W2611804526 title "Adaptation and Learning Over Networks for Nonlinear System Modeling" @default.
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- W2611804526 doi "https://doi.org/10.1016/b978-0-12-812976-0.00013-0" @default.
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