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Neural Networks for Web Content Filtering
September/October 2002 (vol. 17 no. 5)
pp. 48-57

The proliferation of objectionable material on the Internet has created an urgent need for countermeasures to protect unsuspecting children and others from such material's harmful effects. However, current Web content-filtering techniques and commercially available Web-filtering systems have serious shortcomings. Machine intelligence can compensate for these shortcomings. For example, the Intelligent Classification Engine uses neural networks' learning capabilities to provide fast, accurate differentiation between pornographic and nonpornographic Web pages. The engine works with both Kohonen's Self-Organizing Maps and Fuzzy Adaptive Resonance Theory networks. Both networks perform significantly better than nonintelligent techniques; KSOM has greater classification accuracy, but training it takes longer.

Index Terms:
Fuzzy ART, KSOM, neural networks, Web content filtering, Web page classification
Citation:
Pui Y. Lee, Siu C. Hui, Alvis Cheuk M. Fong, "Neural Networks for Web Content Filtering," IEEE Intelligent Systems, vol. 17, no. 5, pp. 48-57, Sept.-Oct. 2002, doi:10.1109/MIS.2002.1039832
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