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Journal of Classification
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10-12-2019

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

Authors: Chengbo Lu, Ying Mei

Published in: Journal of Classification | Issue 3/2020

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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.
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Metadata
Title
An Optimal Weight Semi-Supervised Learning Machine for Neural Networks with Time Delay
Authors
Chengbo Lu
Ying Mei
Publication date
10-12-2019
Publisher
Springer US
Published in
Journal of Classification / Issue 3/2020
Print ISSN: 0176-4268
Electronic ISSN: 1432-1343
DOI
https://doi.org/10.1007/s00357-019-09352-2

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