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In mathematics the signal-to-noise statistic distance between two vectors ''a'' and ''b'' with mean values and and standard deviation and respectively is: : In the case of Gaussian-distributed data and unbiased class distributions, this statistic can be related to classification accuracy given an ideal linear discrimination, and a decision boundary can be derived.〔Auffarth, B., Lopez, M., Cerquides, J. (2010). Comparison of redundancy and relevance measures for feature selection in tissue classification of CT images. Advances in Data Mining. Applications and Theoretical Aspects. p. 248--262. Springer. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.170.1528〕 This distance is frequently used to identify vectors that have significant difference. One usage is in bioinformatics to locate genes that are differential expressed on microarray experiments.〔Pomeroy, S.L. et al. (Gene Expression-Based Classification and Outcome Prediction of Central Nervous System Embryonal Tumors ). Nature 415, 436–442.〕 ==See also== *Distance *Uniform norm *Manhattan distance *Signal-to-noise ratio *Signal to noise ratio (imaging) 抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)』 ■ウィキペディアで「Signal-to-noise statistic」の詳細全文を読む スポンサード リンク
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