Mixture kernel radial basis functions neural networks for web log classification

Dash Ch Sanjeev Kumar, Pandia Manoj Kumar, Dehuri Satchidananda, Cho Sung-Bae

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the immense horizontal and vertical growth of the World Wide Web (WWW), it is becoming more popular for website owners to showcase their innovations, business, and concepts. Along side they are also interested in tracking and understanding the need of the users. Analyzing web access logs, one can understand the browsing behavior of users. However, web access logs are voluminous as well as complex. Therefore, a semi-automatic intelligent analyzer can be used to find out the browsing patterns of a user. Moreover, the pattern which is revealed from this deluge of web access logs must be interesting, useful, and understandable. A radial basis function neural networks (RBFNs) with mixture of kernels are used in this work for classification of web access logs. In this connection two RBFNs with different mixture of kernels are investigated on web access logs for classification. The collected data are used for training, validation, and testing of the models. The performances of these models are compared with RBFNs. It is concluded that mixture of appropriate kernels are an attractive alternative to RBFNs.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Frontiers of Intelligent Computing
Subtitle of host publicationTheory and Applications, FICTA 2012
PublisherSpringer Verlag
Pages1-9
Number of pages9
ISBN (Print)9783642353130
DOIs
Publication statusPublished - 2013 Jan 1
Event1st International Conference on Frontiers in Intelligent Computing: Theory and Applications, FICTA 2012 - Bhubaneswar, Odisa, India
Duration: 2012 Dec 222012 Dec 23

Publication series

NameAdvances in Intelligent Systems and Computing
Volume199 AISC
ISSN (Print)2194-5357

Other

Other1st International Conference on Frontiers in Intelligent Computing: Theory and Applications, FICTA 2012
CountryIndia
CityBhubaneswar, Odisa
Period12/12/2212/12/23

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All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Kumar, D. C. S., Kumar, P. M., Satchidananda, D., & Sung-Bae, C. (2013). Mixture kernel radial basis functions neural networks for web log classification. In Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2012 (pp. 1-9). (Advances in Intelligent Systems and Computing; Vol. 199 AISC). Springer Verlag. https://doi.org/10.1007/978-3-642-35314-7_1