Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Boosting the margin: A new explanation for the effectiveness of voting methods
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
MORF: A Distributed Multimodal Information Filtering System
PCM '02 Proceedings of the Third IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
MORF: A Distributed Multimodal Information Filtering System
PCM '02 Proceedings of the Third IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
An adaptive skin model and its application to objectionable image filtering
Proceedings of the 12th annual ACM international conference on Multimedia
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The proliferation of objectionable information on the Internet has reached a level of serious concern. To empower end-users with the choice of blocking undesirable and offensive Web-sites, we propose a multimodal personalized information filter, named MORF. The design of MORF aims to meet three major performance goals: efficiency, accuracy, and personalization. To achieve these design goals, we have devised a multimodality classification algorithm and a personalization algorithm. Empirical study and initial statistics collected from the MORF filters deployed at sites in the U.S. and Asia show that MORF is both efficient and effective, compared to the traditional URL- and text-based filtering approaches.