Abstract
In this paper, to address the transfer learning problem in big data fields, a self labeling online sequential extreme learning machine is presented, which is called SLOSELM. Firstly, an ELM classifier is trained on the labeled training data set of the source domain. Secondly, the unlabeled data set of the target domain is classified by the ELM classifier. In the third step, the high confident samples are selected and the OSELM is employed to update the original ELM classifier. Tested on the real-world daily activity data set, the results show that our algorithm performs well and can achieve 75 % accuracy, which is about 10 % higher than the traditional ELM itself.
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Notes
- 1.
Source codes and some references of ELM can be found at www.ntu.edu.sg/home/egbhuang.
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Acknowledgements
This work is supported in part by the National Natural Science Foundation of China (Grant No.U1504609), by the Key Scientific and Technological Project of the Higher Education Institutions of He’nan Province, China (Grant No.15A520003).
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Zhao, Z., Liu, L., Li, L., Ma, Q. (2016). SLOSELM: Self Labeling Online Sequential Extreme Learning Machine. In: Li, W., et al. Internet and Distributed Computing Systems. IDCS 2016. Lecture Notes in Computer Science(), vol 9864. Springer, Cham. https://doi.org/10.1007/978-3-319-45940-0_16
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