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首页> 外文会议>Annual Allerton Conference on Communication, Control, and Computing; 20040929-1001; Monticello,IL(US) >Distributed Learning in Wireless Sensor Networks
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Distributed Learning in Wireless Sensor Networks

机译：无线传感器网络中的分布式学习

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摘要

The problem of distributed or decentralized detection and estimation in applications such as wireless sensor networks has often been considered in the framework of parametric models, in which strong assumptions are made about a statistical description of nature. In certain applications, such assumptions are warranted and systems designed from these models show promise. However, in other scenarios, prior knowledge is at best vague and translating such knowledge into a statistical model is undesirable. Applications such as these pave the way for a nonparametric study of distributed detection and estimation. In this paper, we review recent work of the authors in which some elementary models for distributed learning are considered. These models are in the spirit of classical work in nonparametric statistics and are applicable to wireless sensor networks.
机译：通常在参数模型的框架中考虑诸如无线传感器网络之类的应用中的分布式或分散式检测和估计问题，在该模型中，对自然的统计描述进行了强有力的假设。在某些应用中，此类假设是必要的，并且根据这些模型设计的系统具有广阔的前景。但是，在其他情况下，现有知识充其量是模糊的，并且不希望将此类知识转换为统计模型。诸如此类的应用为分布式检测和估计的非参数研究铺平了道路。在本文中，我们回顾了作者的最新工作，其中考虑了一些分布式学习的基本模型。这些模型是非参数统计中经典工作的精神，适用于无线传感器网络。

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