The Dempster-Shafer Theory of Value Confidence and Incomplete Information


The Dempster-Shafer Theory of Value Confidence and Incomplete Information – The paper presents a novel framework for identifying the probability of the occurrence of an event given a set of events in a set of distributions. The idea is to first learn the parameters of the distribution and then use that information to decide whether a distribution will happen. In this work we present an alternative approach, based on conditional random field approximation (CRFA). First, we perform CRFA by computing the posterior distribution for the probability model. Next, we analyze the results of CRFA by comparing it to the posterior distribution and by performing an extensive experimental analysis with the experimental results obtained by using a simulated real-world situation.

This paper proposes an image recognition method that employs a hierarchical representation for categorization and segmentation in order to reduce the number of features for training and segmentation. We proposed a fully Convolutional neural network with a stacked architecture built specifically for classification and segmentation. The structure of this new architecture is described in terms of an evolutionary algorithm with no explicit feature selection or feature tracking. To validate the performance of the proposed method, a detailed analysis of the hierarchical representation with features from different cameras is presented. The proposed architecture can be viewed as an extension of the convolutional neural network architecture, as we show that it does not have any loss or loss with respect to a deep network. Using the new architecture our method performs a high accuracy classification task in ImageNet (7.2%) while performing at least 20.8% accuracy on the classification task as compared to the baseline.

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The Dempster-Shafer Theory of Value Confidence and Incomplete Information

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  • Neural Architectures of Visual Attention

    Stacked Extraction and Characterization of Object Categories from Camera ResidualsThis paper proposes an image recognition method that employs a hierarchical representation for categorization and segmentation in order to reduce the number of features for training and segmentation. We proposed a fully Convolutional neural network with a stacked architecture built specifically for classification and segmentation. The structure of this new architecture is described in terms of an evolutionary algorithm with no explicit feature selection or feature tracking. To validate the performance of the proposed method, a detailed analysis of the hierarchical representation with features from different cameras is presented. The proposed architecture can be viewed as an extension of the convolutional neural network architecture, as we show that it does not have any loss or loss with respect to a deep network. Using the new architecture our method performs a high accuracy classification task in ImageNet (7.2%) while performing at least 20.8% accuracy on the classification task as compared to the baseline.


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