A Data based Approach for Liver and Bone Diseases Prediction


A Data based Approach for Liver and Bone Diseases Prediction – Our understanding of the function of a large set of variables is important for the analysis of complex data. In this work, we propose a new method for the extraction and interpretation of the parameters that is similar to the standard approach of learning function models.

In this paper, the problem of performing nonlinear transformation in a large data set is considered. Three well-known nonlinear transformations are studied: k-nearest neighbors, k-nearest-similarity and nonlinear divergence. The transformations of this transformation are presented which are performed in two different ways: using the data as a vector matrix, and using binary codes as a string. The binary codes are used to select the transformation’s probability, and a measure of the probability of the transformation. This paper is the first attempt to perform binary derivation in nonlinear transformation by learning nonlinear transformations using binary codes. The result shows that the binary codes are the best choice for binary transformation, and thus can be used to classify complex nonlinear transformations. We also propose an algorithm for Bayesian analysis of nonlinear transformations which uses binary codes based on learning the transformation probability. We demonstrate results on simulated data.

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A Data based Approach for Liver and Bone Diseases Prediction

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    The Geometric Model to Simulate Human BehaviorIn this paper, the problem of performing nonlinear transformation in a large data set is considered. Three well-known nonlinear transformations are studied: k-nearest neighbors, k-nearest-similarity and nonlinear divergence. The transformations of this transformation are presented which are performed in two different ways: using the data as a vector matrix, and using binary codes as a string. The binary codes are used to select the transformation’s probability, and a measure of the probability of the transformation. This paper is the first attempt to perform binary derivation in nonlinear transformation by learning nonlinear transformations using binary codes. The result shows that the binary codes are the best choice for binary transformation, and thus can be used to classify complex nonlinear transformations. We also propose an algorithm for Bayesian analysis of nonlinear transformations which uses binary codes based on learning the transformation probability. We demonstrate results on simulated data.


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