Abstract
Regularized least squares twin support vector machines are a new nonparallel hyperplane classifier, which can lead to simple and fast algorithms for generating binary classifiers by replacing inequality constraints with equality constraints and implementing the structural risk minimization principle in twin support vector machines. Multi-view learning is an emerging direction in machine learning which aims to exploit distinct views to improve generalization performance from multiple distinct feature sets. Experimental results demonstrate that our proposed methods are effective.
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Acknowledgments
This work is supported by Ningbo University talent project 421703670 as well as programs sponsored by K.C. Wong Magna Fund in Ningbo University. It is also supported by the Zhejiang Provincial Department of Education under Projects 801700472. It is also supported by the Natural Science Foundation of Zhejiang Province under Projects LQ18F020001.
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Xie, X. Regularized multi-view least squares twin support vector machines. Appl Intell 48, 3108–3115 (2018). https://doi.org/10.1007/s10489-017-1129-3
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DOI: https://doi.org/10.1007/s10489-017-1129-3