An Optimal Weight Semi-Supervised Learning Machine for Neural Networks with Time Delay

Chengbo Lu () and Ying Mei
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Chengbo Lu: Lishui University
Ying Mei: Lishui University

Journal of Classification, 2020, vol. 37, issue 3, No 8, 656-670

Abstract: Abstract In this paper, an optimal weight semi-supervised learning machine for a single-hidden layer feedforward network (SLFN) with time delay is developed. Both input weights and output weights of the SLFN are globally optimized with manifold regularization. By feature mapping, input vectors can be placed at the prescribed positions in the feature space in the sense that the separability of all nonlinearly separable patterns can be maximized, unlabeled data can be leveraged to improve the classification accuracy when labeled data are scarce, and a high degree of recognition accuracy can be achieved with a small number of hidden nodes in the SLFN. Some simulation examples are presented to show the excellent performance of the proposed algorithm.

Keywords: Neural networks; Optimal weight learning; Semi-supervised learning; Manifold regularization; Time delay (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s00357-019-09352-2

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