Abstract
Co-training is a framework for semi-supervised learning that has attracted much attention due to its good performance and easy adaptation for various learning algorithms. In a recent work, Caldas et al. proposed a co-training-based method using the recently proposed supervised learning method named minimal learning machine (MLM). Although the proposed method, referred to as Co-MLM, presented results that are comparable to other semi-supervised algorithms, using MLM as a base learner resulted in a formulation with heavy computational cost. Aiming to mitigate this problem, in this paper, we propose an improved variant of Co-MLM with reduced computational cost on both training and testing phases. The proposed method is compared to Co-MLM and other Co-training-based semi-supervised methods, presenting comparable performances.
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The authors acknowledge the support of CNPq (Grant 456837/2014-0 and research fellowship Grant 305048/2016-3).
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Caldas, W.L., Gomes, J.P.P. & Mesquita, D.P.P. Fast Co-MLM: An Efficient Semi-supervised Co-training Method Based on the Minimal Learning Machine. New Gener. Comput. 36, 41–58 (2018). https://doi.org/10.1007/s00354-017-0027-x
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DOI: https://doi.org/10.1007/s00354-017-0027-x