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An Information Filtering Model Based on Neural Network

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Computational Intelligence and Intelligent Systems (ISICA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 874))

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Abstract

Thorough analysis of the traditional linear model of information filtering, an improved model is proposed based on neural network, which reflects the user’s expectation. Taking 200 Email as the test object, the advantages and disadvantages of the linear model and the improved model are compared. The improved information filtering model has strong self-learning ability and adaptive ability, and improves the recognition rate.

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References

  1. Zhu, L., Bai, L.: Web information filtering technology based on mutual information. Appl. Mech. Mater. 687–691, 2224–2228 (2014)

    Article  Google Scholar 

  2. Hellmich, M.: Statistical inference of a software reliability model by linear filtering. J. Stat. Manage. Syst. 19(2), 163–181 (2016)

    Article  Google Scholar 

  3. Li, T., Corchado, J.M., Bajo, J., et al.: Effectiveness of Bayesian filters: an information fusion perspective. Inf. Sci. 329, 670–689 (2016)

    Article  Google Scholar 

  4. Sven, B., Wei, W., Benjamin, L., et al.: Information filtering in resonant neurons. J. Comput. Neurosci. 39(3), 349 (2015)

    Article  MathSciNet  Google Scholar 

  5. Liu, J.Q., Luo, M.: Research on internet monitoring system based on multi-layer text information filtering method through artificial neural networks. Adv. Mater. Res. 532–533, 1036–1040 (2012)

    Google Scholar 

  6. Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. Comput. Sci. 14(7), 38–39 (2015)

    Google Scholar 

  7. Jia, W., Zhao, D., Shen, T., et al.: An optimized classification algorithm by BP neural network based on PLS and HCA. Appl. Intell. 43(1), 1–16 (2015)

    Article  Google Scholar 

  8. Xu, B., Zhang, H., Wang, Z., et al.: Model and algorithm of BP neural network based on expanded multichain quantum optimization. Math. Probl. Eng. 2015(12), 1–11 (2015)

    MathSciNet  Google Scholar 

  9. Sundermeyer, M., Oparin, I., Gauvain, J.L., et al.: Comparison of feedforward and recurrent neural network language models, pp. 8430–8434 (2013)

    Google Scholar 

  10. Zhang, J., Huang, L., Xu, H., et al.: An incremental BP neural network based spurious message filter for VANET. In: International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, pp. 360–367. IEEE (2012)

    Google Scholar 

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Acknowledgements

The work is supported in part by Department of Education of Guangdong Province under Grant 2015KQNCX188.

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Authors and Affiliations

  1. Department of Computer Science, Guangdong University of Science and Technology, Dongguan, China

    Rongrong Li

Authors
  1. Rongrong Li
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Corresponding author

Correspondence to Rongrong Li .

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Editors and Affiliations

  1. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China

    Kangshun Li

  2. Jiangxi University of Science and Technology, Ganzhou, Jiangxi, China

    Wei Li

  3. Chemical and Petroleum Engineering, University of Calgary, Calgary, Alberta, Canada

    Zhangxing Chen

  4. School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

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Li, R. (2018). An Information Filtering Model Based on Neural Network. In: Li, K., Li, W., Chen, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2017. Communications in Computer and Information Science, vol 874. Springer, Singapore. https://doi.org/10.1007/978-981-13-1651-7_19

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  • DOI: https://doi.org/10.1007/978-981-13-1651-7_19

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1650-0

  • Online ISBN: 978-981-13-1651-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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