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An asymmetry algorithm based on parameter transformation for Hessian matrix

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Abstract

An asymmetry algorithm recognition model, which is presented based on Hessian matrix transform by adjusting the diagonal parameter of Hessian matrix and which does not change the positive semi-definite characteristic, increases or decreases the distance between positive class or negative class and hyperplane on the asymmetry sample set and realizes our intention for separating the small samples more precisely. The experiment indicates that we cannot simply fix the diagonal parameter of Hessian matrix but must dynamically adjust the diagonal weight coefficient for controlling the number of error-divided samples. This modified asymmetry learning algorithm is more superior than the traditional standard SVM method and can recognize DNA sequence and its mean recognition rate comes to 93.3%.

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Acknowledgments

The work is supported by Natural Science Foundation Project of CQ CSTC and Science & Technology Program of ChongQing NanAn.

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Authors and Affiliations

  1. Chongqing Key Laboratory of Electronic Commerce and Supply Chain System, Chongqing Technology and Business University, Chongqing, 400067, China

    Zeju Luo

  2. Economics and Management Center, Chongqing Technology and Business University, Chongqing, 400067, China

    Lihong Song

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  1. Zeju Luo
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  2. Lihong Song
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Correspondence to Zeju Luo.

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Luo, Z., Song, L. An asymmetry algorithm based on parameter transformation for Hessian matrix. Neural Comput & Applic 21, 1545–1550 (2012). https://doi.org/10.1007/s00521-012-0876-7

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  • DOI: https://doi.org/10.1007/s00521-012-0876-7

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