Due to the widening semantic gap of videos, computational tools to classify these videos into different genre are highly needed to\r\nnarrow it. Classifying videos accurately demands good representation of video data and an efficient and effective model to carry\r\nout the classification task. Kernel Logistic Regression (KLR), kernel version of logistic regression (LR), proves its efficiency as a\r\nclassifier, which can naturally provide probabilities and extend tomulticlass classification problems. In this paper,Weighted Kernel\r\nLogistic Regression (WKLR) algorithm is implemented for video genre classification to obtain significant accuracy, and it shows\r\naccurate and faster good results.
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