Abstract
In wireless sensor networks, classification and regression are very fundamental tasks. To reduce and balance the energy consumption of nodes during training classifiers or regression machines, a distributed incremental learning problem of kernel machine by using 1-norm regularization is studied, and an energy-balanced distributed learning algorithm for the sparse kernel machine is proposed. In this proposal, a novel incremental learning algorithm and an energy-balanced node selection strategy that takes into account the residual energy of node, the number of been accessed and the neighbors number of node are used. Simulation results show that this proposal can obtain pretty consistent prediction correct rate with the batch learning algorithm, and it can get a very simple model. Meanwhile, it has significant advantages with respect to the communication costs and the iterations. Moreover, it can reduce and balance the energy consumption of nodes.
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Acknowledgments
This study was funded by the National Natural Science Foundation of China, Project number is 61203377.
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Ji, X., Hou, Y., Hou, C., Gao, F., Wang, S. (2017). Energy-Balanced Distributed Sparse Kernel Machine in Wireless Sensor Network. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10634. Springer, Cham. https://doi.org/10.1007/978-3-319-70087-8_64
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DOI: https://doi.org/10.1007/978-3-319-70087-8_64
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