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2018 | OriginalPaper | Buchkapitel

Multi-view Multi-task Support Vector Machine

verfasst von : Jiashuai Zhang, Yiwei He, Jingjing Tang

Erschienen in: Computational Science – ICCS 2018

Verlag: Springer International Publishing

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Abstract

Multi-view Multi-task (MVMT) Learning, a novel learning paradigm, can be used in extensive applications such as pattern recognition and natural language processing. Therefore, researchers come up with several methods from different perspectives including graph model, regularization techniques and feature learning. SVMs have been acknowledged as powerful tools in machine learning. However, there is no SVM-based method for MVMT learning. In order to build up an excellent MVMT learner, we extend PSVM-2V model, an excellent SVM-based learner for MVL, to the multi-task framework. Through experiments we demonstrate the effectiveness of the proposed method.

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Titel: Multi-view Multi-task Support Vector Machine
verfasst von: Jiashuai Zhang
Yiwei He
Jingjing Tang
Verlag: Springer International Publishing
Buch: Computational Science – ICCS 2018
Print ISBN: 978-3-319-93700-7

Electronic ISBN: 978-3-319-93701-4

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-93701-4_32

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