The nature of statistical learning theory
The nature of statistical learning theory
Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
One-class svms for document classification
The Journal of Machine Learning Research
Estimating the Support of a High-Dimensional Distribution
Neural Computation
A Bayesian discriminating features method for face detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
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To solve the low sampling efficiency problem of negative samples in object detection and information retrieval, a cascaded mixture SVM classifier along with its learning method is proposed in this paper. The classifier is constructed by cascading one-class SVC and two-class SVC. In the learning method, first, 1SVC is trained by using the cluster features of the positive samples, then the 1SVC trained is used to collect the negative samples close to the positive samples and to eliminate the outlier positive samples, finally, the 2SVC is trained by using the positive samples and effective negative samples collected. The cascaded mixture SVM classifier integrates the merits of both 1SVC and 2SVC, and has the characters of higher detection rate and lower false positive rate, and is suitable for object detection and information retrieval. Experimental results show that the cascaded SVM classifier outperforms traditional classifiers.