Distributed semi-supervised learning algorithms for random vector functional-link networks with distributed data splitting across samples and features

Distributed semi-supervised learning algorithms for random vector functional-link networks with distributed data splitting across samples and features

In this paper, we propose two manifold regularization (MR) based distributed semi-supervised learning (DSSL) algorithms using the random vector functional link (RVFL) network and alternating direction method of multipliers (ADMM) strategy. In DSSL problems, training data consisting of labeled and un...

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Bibliographic Details
Main Authors: Xie, J., Liu, S., Dai, H., Rong, Yue
Format: Journal Article
Language:English
Published: ELSEVIER 2020
Subjects:
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science
Distributed learning (DL)
Semi-supervised learning (SSL)
Manifold regularization (MR)
Alternating direction method of multipliers (ADMM)
Random vector functional link (RVFL)
Distributed features
NEURAL-NETWORK
Online Access:http://hdl.handle.net/20.500.11937/88936

Internet

http://hdl.handle.net/20.500.11937/88936

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