A Deep Learning Approach for Video Classification Based on Convolutional Neural Network


A Deep Learning Approach for Video Classification Based on Convolutional Neural Network – We propose a deep CNN-based framework for object classification. The proposed method, called MCPI, tackles object classification problems in an objective way. While other approaches to object classification have been proposed, MCPI provides an objective way that provides a more comprehensive view of existing object classification approaches. We provide a comprehensive review of existing object classification approaches and provide an overview of MCPI for several benchmark tasks. MCPI achieves the state of the art on several tasks, including video classification, segmentation and object detection, which is in contrast to state-of-the-art methods.

We propose a new approach for stochastic dual coordinate descent (DirCd). We show that there exists a principled upper bound for the convergence rate of the DirCd algorithm. Moreover, the convergence rate has nonzero upper and lower bound on the mean-field of the algorithm. With the nonzero lower bound, as well as the nonconvex upper bound on the mean-field, our algorithm is able to guarantee convergence to the target point and to the optimal solution at any point in the optimal solution space and with high variance. We conduct experimental evaluation of the proposed DirCd algorithm on the MNIST dataset and show that the proposed DirCd algorithm achieves similar or better performance than other gradient descent algorithm in the datasets.

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A Deep Learning Approach for Video Classification Based on Convolutional Neural Network

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  • A Novel Feature Selection Framework for Face Recognition Using Generative Adversarial Networks

    Stochastic Dual Coordinate Ascent via Convex Expansion ConstraintWe propose a new approach for stochastic dual coordinate descent (DirCd). We show that there exists a principled upper bound for the convergence rate of the DirCd algorithm. Moreover, the convergence rate has nonzero upper and lower bound on the mean-field of the algorithm. With the nonzero lower bound, as well as the nonconvex upper bound on the mean-field, our algorithm is able to guarantee convergence to the target point and to the optimal solution at any point in the optimal solution space and with high variance. We conduct experimental evaluation of the proposed DirCd algorithm on the MNIST dataset and show that the proposed DirCd algorithm achieves similar or better performance than other gradient descent algorithm in the datasets.


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