Proceedings of the 3rd International Conference on Mechatronics and Industrial Informatics

An Incremental Learning Method for L1- Regularized Kernel Machine in WSN

Authors
Xin-Rong Ji, Cui-Qin Hou, Yi-Bin Hou, Da Li
Corresponding Author
Xin-Rong Ji
Available Online October 2015.
DOI
10.2991/icmii-15.2015.71How to use a DOI?
Keywords
Kernel Machine; Incremental Learning Method; L1 Regularized; Wireless Sensor Network (WSN)
Abstract

Due to the limited energy, memory space and processing ability on wireless sensor nodes, the batch learning method will be infeasible for larger number of samples or sequence samples. This paper focuses on the incremental learning method for kernel machine by involving L1 regularized, a novel incremental learning algorithm for L1 regularized Kernel Minimum Squared Error machine (L1-KMSE-Increm) is proposed and evaluated on both synthetic and real data sets. The simulation results reveal that L1-KMSE-Increm algorithm can obtain almost the same prediction accuracy as that of corresponding batch learning method, and significantly outperforms the competitor on the sparse ratio of model and the running time.

Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 3rd International Conference on Mechatronics and Industrial Informatics
Series
Advances in Computer Science Research
Publication Date
October 2015
ISBN
978-94-6252-131-5
ISSN
2352-538X
DOI
10.2991/icmii-15.2015.71How to use a DOI?
Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Xin-Rong Ji
AU  - Cui-Qin Hou
AU  - Yi-Bin Hou
AU  - Da Li
PY  - 2015/10
DA  - 2015/10
TI  - An Incremental Learning Method for L1- Regularized Kernel Machine in WSN
BT  - Proceedings of the 3rd International Conference on Mechatronics and Industrial Informatics
PB  - Atlantis Press
SP  - 397
EP  - 403
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmii-15.2015.71
DO  - 10.2991/icmii-15.2015.71
ID  - Ji2015/10
ER  -