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In computer vision, image datasets used for classification are naturally associated with multiple labels and comprised of multiple views, because each image may contain several objects (e.g., pedestrian, bicycle, and tree) and is properly characterized by multiple visual features (e.g., color, texture, and shape). Currently, available tools ignore either the label relationship or the view complementarily...
A method to determine C,γ , the hyper-parameters, range for Radial Basis Function Support Vector Machines (RBF SVMs) is proposed. The γ range is determined by the extreme Squared Euclidean Distance (SED) quantiles of the training set, and the C range is determined by one pass whole training set training decreasingly along logγmax to the over-regularized limit first and increasingly along logγmedian...
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