A hybrid training mechanism for applying neural networks to web-based applications

Ko Kang Chu, Maiga Chang, Yen Teh Hsia

Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

3 Citations (Scopus)

Abstract

This paper proposes a hybrid training neural network and applying it to the Accuracy Counter (AC) developed previously. The neural network is used for detecting the cheating model for abnormal browsing behaviors performed by users in the conflicting environment. The most significant issue, training, should be taken into consideration while we are applying the neural network to web-based applications such like the Accuracy Counter. Therefore, we design a hybrid web-based training mechanism for neural networks to deal with this kind of training problem. Finally, we also find out that the AC's block rate for detecting the abnormal browsing behaviors is increasing from 61% (rule-based) to 76% (neural networks with hybrid training mechanism) in the efficient and acceptable training period.

Original languageEnglish
Title of host publication2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004
Pages3543-3547
Number of pages5
DOIs
Publication statusPublished - 2004
Event2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004 - The Hague, Netherlands
Duration: 10 Oct. 200413 Oct. 2004

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume4
ISSN (Print)1062-922X

Conference

Conference2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004
Country/TerritoryNetherlands
CityThe Hague
Period10/10/0413/10/04

Keywords

  • Neural network
  • Online training
  • Web counter

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