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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/103197


    Title: Output Effect Evaluation Based on Input Features in Neural Incremental Attribute Learning for Better Classification Performance
    Authors: Ting Wang;Guan, Sheng-Uei;Ka Lok Man;Jong Hyuk Park;Hsu, Hui-Huang
    Contributors: 淡江大學資訊工程學系
    Keywords: pattern classification;neural networks;incremental attribute learning;feature ordering;discrimination ability
    Date: 2014-12-29
    Issue Date: 2015-05-21
    Publisher: Basel: M D P I AG
    Abstract: Machine learning is a very important approach to pattern classification. This paper provides a better insight into Incremental Attribute Learning (IAL) with further analysis as to why it can exhibit better performance than conventional batch training. IAL is a novel supervised machine learning strategy, which gradually trains features in one or more chunks. Previous research showed that IAL can obtain lower classification error rates than a conventional batch training approach. Yet the reason for that is still not very clear. In this study, the feasibility of IAL is verified by mathematical approaches. Moreover, experimental results derived by IAL neural networks on benchmarks also confirm the mathematical validation.
    Relation: Symmetry 7(1), pp.53-66
    DOI: 10.3390/sym7010053
    Appears in Collections:[資訊工程學系暨研究所] 期刊論文

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