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Multi-view Multi-task Support Vector Machine

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10861)

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.

Keywords

SVM-based MVMT learning PSVM-2V Regularization method 

Notes

Acknowledgments

This work has been partially supported by grants from National Natural Science Foundation of China (Nos. 61472390, 71731009, 71331005, and 91546201), and the Beijing Natural Science Foundation (No. 1162005).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mathematical SciencesUniversity of Chinese Academy of ScienceBeijingChina
  2. 2.School of Computer and Control EngineeringUniversity of Chinese Academy of ScienceBeijingChina

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