A new family of proximity graphs: class cover catch digraphs
Discrete Applied Mathematics
A Hierarchy of Support Vector Machines for Pattern Detection
The Journal of Machine Learning Research
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We describe a novel extension to the Class-Cover-Catch-Digraph (CCCD)classifier, specifically tuned to detection problems. These are two-class classificationproblems where the natural priors on the classes are skewed by several orders of magnitude.The emphasis of the proposed techniques is in computationally efficient classificationfor real-time applications. Our principal contribution consists of two boosted classi-fiers built upon the CCCD structure, one in the form of a sequential decision process andthe other in the form of a tree. Both of these classifiers achieve performances comparableto that of the original CCCD classifiers, but at drastically reduced computational expense.An analysis of classification performance and computational cost is performed using datafrom a face detection application. Comparisons are provided with Support Vector Machines(SVM) and reduced SVMs. These comparisons show that while some SVMs mayachieve higher classification performance, their computational burden can be so high as tomake them unusable in real-time applications. On the other hand, the proposed classifierscombine high detection performance with extremely fast classification.