The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Digital Image Processing
Incremental Induction of Decision Trees
Machine Learning
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Incremental Learning with Support Vector Machines
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
System for screening objectionable images
Computer Communications
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In this paper we propose an on-line learning system for objectionable image filtering. Firstly, the system applies a robust skin detector to generate skin mask image, then features of color, skin texture and shape are extracted. Secondly these features are inputted into an on-line incremental learning module, which derives from support vector machine. The most difference between this method and other online SVM is that the new algorithm preserves not only support vectors but also the cases with longest distance from the decision surface, because the more representative patterns are the farthest examples away from the hyperplane. Our system is tested on about 70000 images download from the Internet. Experimental results demonstrate the good performance when compared with other on-line learning method.