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Published in:
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2019 | OriginalPaper | Chapter

1. Introduction

Authors : Shiliang Sun, Liang Mao, Ziang Dong, Lidan Wu

Published in: Multiview Machine Learning

Publisher: Springer Singapore

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Abstract

In this chapter, we first give the background for writing this monograph. Then, we provide a formal definition of multiview machine learning and discuss its difference and similarities with related concepts data fusion and multimodal learning. After showcasing four typical application fields in artificial intelligence, we explain the underlying philosophy on why multiview learning is useful. Finally, we give the organization structure of the book.

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next chapter Multiview Semi-supervised Learning
Literature
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Chao G, Sun S (2016) Alternative multiview maximum entropy discrimination. IEEE Trans Neural Netw Learn Syst 27(7):1445–1456MathSciNetCrossRef
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Sun S (2013) A survey of multi-view machine learning. Neural Comput Appl 23(7–8):2031–2038CrossRef
Sun S, Chao G (2013) Multi-view maximum entropy discrimination. In: Proceedings of the 23rd international joint conference on artificial intelligence, pp 1706–1712
Sun S, Shawe-Taylor J, Mao L (2017) Pac-bayes analysis of multi-view learning. Inf Fusion 35:117–131CrossRef
Zhao J, Xie X, Xu X, Sun S (2017) Multi-view learning overview: Recent progress and new challenges. Inf Fusion 38:43–54CrossRef
Metadata
Title
Introduction
Authors
Shiliang Sun
Liang Mao
Ziang Dong
Lidan Wu
Copyright Year
2019
Publisher
Springer Singapore
Book
Multiview Machine Learning

Print ISBN: 978-981-13-3028-5

Electronic ISBN: 978-981-13-3029-2

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-981-13-3029-2_1

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