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2016 | OriginalPaper | Chapter

Prediction on Internet Safety Situation of Relevance Vector Machine about GP-RVM Kernel Function

Authors : Xiaolan Xie, Zhen Long, Fahui Gu

Published in: Computational Intelligence and Intelligent Systems

Publisher: Springer Singapore

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Abstract

In prediction of network security situation, the prediction accuracy of traditional single kernel function vector machine is a little low. It can’t describe the randomness and abruptness, and it has some limitation. A network security forecasting model was put forward which combined Gaussian kernel function and polynomial kernel to solve this problem. Proved by simulation experiment, this model can increase prediction accuracy and it has some practical meaning.

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Metadata
Title
Prediction on Internet Safety Situation of Relevance Vector Machine about GP-RVM Kernel Function
Authors
Xiaolan Xie
Zhen Long
Fahui Gu
Copyright Year
2016
Publisher
Springer Singapore
Book
Computational Intelligence and Intelligent Systems

Print ISBN: 978-981-10-0355-4

Electronic ISBN: 978-981-10-0356-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-981-10-0356-1_76

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