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A survey of transfer learning

Journal of big data, 2016-05, Vol.3 (1), Article 9 [Peer Reviewed Journal]

The Author(s) 2016 ;ISSN: 2196-1115 ;EISSN: 2196-1115 ;DOI: 10.1186/s40537-016-0043-6

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  • Title:
    A survey of transfer learning
  • Author: Weiss, Karl ; Khoshgoftaar, Taghi M. ; Wang, DingDing
  • Subjects: Communications Engineering ; Computational Science and Engineering ; Computer Science ; Data Mining and Knowledge Discovery ; Database Management ; Information Storage and Retrieval ; Mathematical Applications in Computer Science ; Networks ; Survey Paper
  • Is Part Of: Journal of big data, 2016-05, Vol.3 (1), Article 9
  • Description: Machine learning and data mining techniques have been used in numerous real-world applications. An assumption of traditional machine learning methodologies is the training data and testing data are taken from the same domain, such that the input feature space and data distribution characteristics are the same. However, in some real-world machine learning scenarios, this assumption does not hold. There are cases where training data is expensive or difficult to collect. Therefore, there is a need to create high-performance learners trained with more easily obtained data from different domains. This methodology is referred to as transfer learning. This survey paper formally defines transfer learning, presents information on current solutions, and reviews applications applied to transfer learning. Lastly, there is information listed on software downloads for various transfer learning solutions and a discussion of possible future research work. The transfer learning solutions surveyed are independent of data size and can be applied to big data environments.
  • Publisher: Cham: Springer International Publishing
  • Language: English
  • Identifier: ISSN: 2196-1115
    EISSN: 2196-1115
    DOI: 10.1186/s40537-016-0043-6
  • Source: Open Access: DOAJ Directory of Open Access Journals
    AUTh Library subscriptions: ProQuest Central
    ROAD
    Springer OA刊
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