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We present a probabilistic ranking-driven classifier for the detection of video semantic concept, such as airplane, building, etc. Most existing concept detection systems utilize Support Vector Machines (SVM) to perform the detection and ranking of retrieved video shots. However, the margin maximization principle of SVM does not perform ranking optimization but merely classification error minimization. To tackle this problem, we exploit the sparse Bayesian kernel model, namely the relevancedoi:10.1145/1386352.1386378 dblp:conf/civr/ZhengNCT08 fatcat:66as6qbcqzdpfahxlqkcy2k6vm