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Learning from data streams using kernel least-mean-square with multiple kernel-sizes and adaptive step-size
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DOI：10.1016/j.neucom.2019.01.055.png)
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摘要
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A learning task is sequential if its data samples become available over time; kernel adaptive filters (KAFs) are sequential learning algorithms. There are three main challenges in KAFs: (1) selection of an appropriate Mercer kernel; (2) the lack of an effective method to determine kernel-sizes in an online learning context; (3) how to tune the step-size parameter. This work introduces a framework for online prediction that addresses the latter two of these open challenges. The kernel-sizes, unlike traditional KAF formulations, are both created and updated in an online sequential way. Further, to improve convergence time, we propose an adaptive step-size strategy that minimizes the mean-square-error (MSE) using a stochastic gradient algorithm. The proposed framework has been tested on three real-world data sets; results show both faster convergence to relatively low values of MSE and better accuracy when compared with KAF-based methods, long short-term memory, and recurrent neural networks. (C) 2019 Elsevier B.V. All rights reserved.
Keyword:
Learning from data streams
Sequence prediction
Kernel least-mean-square
Kernel-size
Step-size
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IF:
6.5
论文数:
2.5W
被引数:
6.5W
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NEUROCOMPUTING
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