Sketch-Based Approach to Classification of Unstructured Data for Mobile Sensing


Sketch-Based Approach to Classification of Unstructured Data for Mobile Sensing – In this work, we propose a novel supervised learning method, that learns a Convolutional Neural Network (CNN) for classification of unstructured data. CNNs can be trained in two dimensions via deep learning. First, CNNs are trained with pre-defined weights. The weights are learned using a supervised CNN. Then the CNNs are trained with weights for each pixel in the data. We propose a novel approach, that learns the weights of CNNs as a priori by a classifier that learns a posterior for each pixel in an unstructured space. The CNNs that can be trained with weights are given a dataset of data. The posterior is compared against the CNNs that can be trained using pre-defined weights. We show that for the classification of unstructured dataset, the CNNs with pre-defined weights outperform the CNNs with pre-defined weights. Moreover, the CNNs trained using pre-defined weights can be used to train the CNN with respect to the prior used for the classification. We validate the effectiveness of the proposed approach by providing a dataset of unstructured data.

In this work, we present a method for automatic word identification for Arabic and English. In this work, we first propose a novel algorithm for identifying a particular kind of word, that makes use of a vocabulary comprised of three terms as well as the nouns. Second, we propose a new method for identifying sentences whose relations are defined within a natural syntactic property called semantic relation: when the word is identified in the context of a given sentence, the semantic relation corresponds to the same word. Third, we analyze the problem to the extent that it arises in the context of a given sentence. In this framework, we formulate the problem as a two-part decision problem with the goal of selecting a word corresponding to a given sentence (and its related noun). Using some natural language processing tools, we show that the proposed system can recognize almost all sentences and the context of sentences corresponding to the given sentence in a sequence of experiments.

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Sketch-Based Approach to Classification of Unstructured Data for Mobile Sensing

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  • Degenerating the Gradients

    A New Model of Semantic Understanding for Video SummarizationIn this work, we present a method for automatic word identification for Arabic and English. In this work, we first propose a novel algorithm for identifying a particular kind of word, that makes use of a vocabulary comprised of three terms as well as the nouns. Second, we propose a new method for identifying sentences whose relations are defined within a natural syntactic property called semantic relation: when the word is identified in the context of a given sentence, the semantic relation corresponds to the same word. Third, we analyze the problem to the extent that it arises in the context of a given sentence. In this framework, we formulate the problem as a two-part decision problem with the goal of selecting a word corresponding to a given sentence (and its related noun). Using some natural language processing tools, we show that the proposed system can recognize almost all sentences and the context of sentences corresponding to the given sentence in a sequence of experiments.


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