Efficient Large Scale Supervised Classification via Randomized Convex Optimization


Efficient Large Scale Supervised Classification via Randomized Convex Optimization – Learning deep representations of image content is a challenging and challenging problem. Recent approaches to this task, based on Deep Convolutional Networks (DCN) and DenseGAN, have been successful and have achieved good results. In this work, we propose a method for learning deep representations of scene images, where each image is represented as a vector of points. Given a vector of points and a vector of hidden units, we propose a novel and efficient method for learning deep representations of this representation space. Our proposed method learns and classifies frames into a set of labels using a convolutional neural network. We show that the classification accuracy of the proposed deep representation can be improved significantly by using the input images on a standard ConvNet. Compared with the state-of-the-art DCN classification metrics and datasets, our method achieves better classification accuracy with a reduction of approximately ten fold in the classification times and up to twenty times larger accuracy when compared to the state-of-the-art DCN metrics.

This paper proposes a new nonparametric method for the clustering of objects in a sequence of high-dimensional data. The method uses the maximum of all variables in a sequence as the clustering metric and then the clusters are clustered using the best clustering criterion. This method is compared with several existing methods that use multiple feature maps, and an innovative data-parallelism method has been incorporated to the clustering. The experimental results demonstrate that the proposed method significantly outperforms the existing methods.

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Efficient Large Scale Supervised Classification via Randomized Convex Optimization

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  • Stacked Neural Networks for Semantic Segmentation of Hyperspectral Images

    Online Learning of Hierarchical Classification ModelsThis paper proposes a new nonparametric method for the clustering of objects in a sequence of high-dimensional data. The method uses the maximum of all variables in a sequence as the clustering metric and then the clusters are clustered using the best clustering criterion. This method is compared with several existing methods that use multiple feature maps, and an innovative data-parallelism method has been incorporated to the clustering. The experimental results demonstrate that the proposed method significantly outperforms the existing methods.


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