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Transfer Learning beyond Text Classification

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Book cover Advances in Machine Learning (ACML 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5828))

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Abstract

Transfer learning is a new machine learning and data mining framework that allows the training and test data to come from different distributions or feature spaces. We can find many novel applications of machine learning and data mining where transfer learning is necessary. While much has been done in transfer learning in text classification and reinforcement learning, there has been a lack of documented success stories of novel applications of transfer learning in other areas. In this invited article, I will argue that transfer learning is in fact quite ubiquitous in many real world applications. In this article, I will illustrate this point through an overview of a broad spectrum of applications of transfer learning that range from collaborative filtering to sensor based location estimation and logical action model learning for AI planning. I will also discuss some potential future directions of transfer learning.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong

    Qiang Yang

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  1. Qiang Yang
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Editor information

Editors and Affiliations

  1. National Laboratory for Novel Software Technology, Nanjing University, 22 Hankou Road, 210093, Nanjing, China

    Zhi-Hua Zhou

  2. The Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, 567, Osaka, Ibaraki, Japan

    Takashi Washio

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© 2009 Springer-Verlag Berlin Heidelberg

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Yang, Q. (2009). Transfer Learning beyond Text Classification. In: Zhou, ZH., Washio, T. (eds) Advances in Machine Learning. ACML 2009. Lecture Notes in Computer Science(), vol 5828. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05224-8_3

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  • DOI: https://doi.org/10.1007/978-3-642-05224-8_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05223-1

  • Online ISBN: 978-3-642-05224-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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