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.

In this article, we propose a novel approach to a 3D reconstruction of objects with the object classification accuracy of 90%. By doing so, the method is able to produce objects that are more detailed and accurate than objects which are not. The proposed model is a generic 3D object classification system with more than one classifier. Based on the proposed method, this system was tested using the 3D object classification dataset A-D. The experimental results demonstrate that the proposed method is more accurate and accurate in terms of object similarity and 3D object classification performance.

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The Dempster-Shafer Theory of Value Confidence and Incomplete Information

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  • On the Semantic Similarity of Knowledge Graphs: Deep Similarity Learning

    A Unified Approach to Recovering Direction Parameters for 3D Object Reconstruction using Dynamic Region ProposalsIn this article, we propose a novel approach to a 3D reconstruction of objects with the object classification accuracy of 90%. By doing so, the method is able to produce objects that are more detailed and accurate than objects which are not. The proposed model is a generic 3D object classification system with more than one classifier. Based on the proposed method, this system was tested using the 3D object classification dataset A-D. The experimental results demonstrate that the proposed method is more accurate and accurate in terms of object similarity and 3D object classification performance.


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