Hybrid model of clustering and kernel autoassociator for reliable vehicle type classification
Machine Vision and Applications
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This paper develops a cascade of linear SVM classifiers for fast object detection. The learning problem of every node in the cascade structure is described as a new quadratic programming problem in the frame of SVM, which makes every linear classifier achieve very high detection rate but only moderate false positive rate. The real experiment shows that this method enjoys good generalization capacity and much fast speed compared with the traditional SVMs.