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    Distributed Incremental wLPSVM Learning

    Zhu, Lei; Ban, Tao; Ikeda, K.; Pang, P.; Sarrafzadeh, Hossein

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    Distributed_Incremental_wLPSVM_Learning.pdf (338.2Kb)
    Date
    2016-12
    Citation:
    Zhu, L., Ban, T., Ikeda, K., Pang, P., & Sarrafzadeh, A. (2016, December). Distributed Incremental wLPSVM Learning. - (Ed.), IEEE Symposium Series on Computational Intelligence (SSCI) (paper 45)
    Permanent link to Research Bank record:
    https://hdl.handle.net/10652/3834
    Abstract
    Weighted linear proximal support vector machine (wLPSVM) is known as an efficient binary classification algorithm with good accuracy and class-imbalance robustness. In this work, original batch wLPSVM is facilitated with distributed incremental learning capability, which allows simultaneously learning from multiple streaming data sources that are geographically distributed. In our approach, incremental and distributed learning are solved as a merging problem at the same time. A new wLPSVM expression is derived. In the new expression, knowledge from samples are presented as a set of class-wised core matrices, and merging knowledge from two subsets of data can be simply accomplished by matrix addition. With the new expression, we are able to conduct incremental and distributed learning at the same time via merging knowledge from multiple incremental stages and multiple data sources.
    Keywords:
    wLPSVM, weighted linear proximal support vector machines, class imbalance learning, distributed incremental learning
    ANZSRC Field of Research:
    170203 Knowledge Representation and Machine Learning
    Copyright Holder:
    Authors
    Rights:
    This digital work is protected by copyright. It may be consulted by you, provided you comply with the provisions of the Act and the following conditions of use: Any use you make of these documents or images must be for research or private study purposes only, and you may not make them available to any other person. You will recognise the author's and publishers rights and give due acknowledgement where appropriate.
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    • Computing Conference Papers [147]

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