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A New Cache Replacement Algorithm in SMO

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Pattern Recognition with Support Vector Machines (SVM 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2388))

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Abstract

In the methods for training Support Vector Machines (SVM), precomputed elements in the Hessian matrix are usually cached in order to avoid recomputation. However, the least-recent-used replacement algorithm that is widely used is not suitable since the elements are requested in an irregular way. A new cache replacement algorithm applied in Sequential Minimal Optimization (SMO) is presented in the paper. The item corresponding to the component with minimal violation of the Karush-Kuhn-Tucher (KKT) condition is deleted to make room for new one when the cache is full. It is shown in the experiments that the hit ratio of the cache is improved compared with LRU cache while the training time can be reduced in the tasks where the computation of elements in Hessian matrix is very time-consuming.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Technology State Key Laboratory of Intelligent Technology and Systems, Tsinghua University, Beijing, 100084, China

    Jianmin Li, Bo Zhang & Fuzong Lin

Authors
  1. Jianmin Li
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  2. Bo Zhang
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  3. Fuzong Lin
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Korea University, Anam-dong, Seongbuk-ku, Seoul, 136-701, Korea

    Seong-Whan Lee

  2. Dipartimento di Informatica e Scienze dell’Informazione, Università di Genova, Via Dodecaneso 35, 16146, Genova, Italy

    Alessandro Verri

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© 2002 Springer-Verlag Berlin Heidelberg

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Li, J., Zhang, B., Lin, F. (2002). A New Cache Replacement Algorithm in SMO. In: Lee, SW., Verri, A. (eds) Pattern Recognition with Support Vector Machines. SVM 2002. Lecture Notes in Computer Science, vol 2388. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45665-1_27

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  • DOI: https://doi.org/10.1007/3-540-45665-1_27

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44016-1

  • Online ISBN: 978-3-540-45665-0

  • eBook Packages: Springer Book Archive

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