Functional estimation in Hilbert space for distributed learning in wireless sensor networks
Functional estimation in Hilbert space for distributed learning in wireless sensor networks
复制标题
无线传感器网络分布式学习的希尔伯特空间函数估计
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
F. Vincent
中科院分区：
文献类型：
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作者：
P. Honeine;C. Richard;J. Bermudez;H. Snoussi;Mehdi Essoloh;F. Vincent
在本文中，我们提出了一种分布式学习策略在无线传感器网络。利用基于核的机器学习的最新发展，我们考虑一个新的稀疏化标准在线学习。相对于以前推导的标准，它是基于估计的误差，因此是非常适合于跟踪系统随时间的演变。我们还推导出一个梯度下降算法，我们证明了它的相关性，以估计在一个给定的区域中的温度的动态演变。
In this paper, we propose a distributed learning strategy in wireless sensor networks. Taking advantage of recent developments on kernel-based machine learning, we consider a new sparsification criterion for online learning. As opposed to previously derived criteria, it is based on the estimated error and is therefore is well suited for tracking the evolution of systems over time. We also derive a gradient descent algorithm, and we demonstrate its relevance to estimate the dynamic evolution of temperature in a given region.