Linear classifier design under heteroscedasticity in Linear Discriminant Analysis
نویسندگان
چکیده
منابع مشابه
Linear classifier design under heteroscedasticity in Linear Discriminant Analysis
Under normality and homoscedasticity assumptions, Linear Discriminant Analysis (LDA) is known to be optimal in terms of minimising the Bayes error for binary classification. In the heteroscedastic case, LDA is not guaranteed to minimise this error. Assuming heteroscedasticity, we derive a linear classifier, the Gaussian Linear Discriminant (GLD), that directly minimises the Bayes error for bina...
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ژورنال
عنوان ژورنال: Expert Systems with Applications
سال: 2017
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2017.02.039