A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
C4.5: programs for machine learning
C4.5: programs for machine learning
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Machine Learning
Neural Networks for Web Content Filtering
IEEE Intelligent Systems
Machine Learning
Classifying Objectionable Websites Based on Image Content
IDMS '98 Proceedings of the 5th International Workshop on Interactive Distributed Multimedia Systems and Telecommunication Services
Structured multimedia document classification
Proceedings of the 2003 ACM symposium on Document engineering
Classifying offensive sites based on image content
Computer Vision and Image Understanding - Special issue on color for image indexing and retrieval
WebGuard: A Web Filtering Engine Combining Textual, Structural, and Visual Content-Based Analysis
IEEE Transactions on Knowledge and Data Engineering
Blocking objectionable web content by leveraging multiple information sources
ACM SIGKDD Explorations Newsletter
Neural Networks
The Role of URLs in Objectionable Web Content Categorization
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Recognition of Pornographic Web Pages by Classifying Texts and Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Applying a Novel Combined Classifier for Hypertext Classification in Pornographic Web Filtering
ICICSE '08 Proceedings of the 2008 International Conference on Internet Computing in Science and Engineering
Combining Classifiers for Web Violent Content Detection and Filtering
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Using a Semi-automatic Keyword Dictionary for Improving Violent Web Site Filtering
SITIS '07 Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System
Horror image recognition based on emotional attention
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
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The development of the Web has been paralleled by the proliferation of harmful Web pages content. Using Violent Web page as a case study, we review some existing solutions, then we propose a violent Web content detection and filtering system called "WebAngels filter" which uses textual and structural analysis. "WebAngels filter" has the advantage of combining several data mining algorithms for Web site classification. We present a comparative study of different data mining techniques to block violent contentWeb pages. Also, we discuss how the combination learning based methods can improve filtering performances. Our results show that it can detect and filter violent content effectively.