A Jensen-Shannon Fuzzy Divergence Measure with Applications under Machine Learning

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Tamanna, Satish Kumar

Abstract

The requirement of appropriate divergence measures emerge as they play a vital part in segregation of two likelihood disseminations. The display communication is committed to the presentation of one such divergence measure utilizing Jensen inequality and Shannon entropy and its approval. On basis of the proposed divergence measure, a new dissimilarity measure is presented. Other than building up approval, a few of its major properties are moreover presented. Advance, based on proposed dissimilarity measure, a new multiple attribute decision making method is presented and is altogether clarified with the assistance of an outlined case. In last paper is briefed with an application of the proposed dissimilarity measure in pattern recognition which is data analysis method uses machine learning algorithms to recognize the patterns.

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