Fast Multi-scale Deep Learning for Video Classification

Fast Multi-scale Deep Learning for Video Classification – In a nutshell, we propose a simple, yet effective method for online feature extraction in video. The main idea is to extract a set of features into the hidden variable space without using any external knowledge. We show results on both human and machine learning datasets that show that the proposed method achieves competitive predictions in a variety of video contexts, and that it outperforms state-of-the-art methods by a significant margin.

In this paper, we propose a new method for evaluating linear regression, called Bayes Linear Regression (BLR). We generalize the linear regression model to use the feature-vector model or the data. We show that the BLR algorithm performs better than the other state-of-the-art methods that perform the same, which can be obtained from the regression literature. We conduct extensive experiments on real-world datasets showing the efficiency and effectiveness of BLR algorithm by comparison to state-of-the-art methods.

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Fast Multi-scale Deep Learning for Video Classification

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  • On the Impact of Negative Link Disambiguation of Link Profiles in Text Streams

    Hierarchical regression using the maximum of all-parts correlationIn this paper, we propose a new method for evaluating linear regression, called Bayes Linear Regression (BLR). We generalize the linear regression model to use the feature-vector model or the data. We show that the BLR algorithm performs better than the other state-of-the-art methods that perform the same, which can be obtained from the regression literature. We conduct extensive experiments on real-world datasets showing the efficiency and effectiveness of BLR algorithm by comparison to state-of-the-art methods.


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