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- W2040396846 abstract "Abstract An upperbound to the probability of error per class in a multivariate pattern classification is derived. The bound, given by P(E|class w i )≤ N R 2 i is derived with minimal assumptions; specifically the mean vectors exist and are distinct and the covariance matrices exist and are non-singular. No other assumptions are made about the nature of the distributions of the classes. In equation (i) N is the number of features in the feature (vector) space and R i is a measure of the “radial neighbourhood” of a class. An expression for R i is developed. A comparison to the multivariate Gaussian hypothesis is presented." @default.
- W2040396846 created "2016-06-24" @default.
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- W2040396846 date "1978-01-01" @default.
- W2040396846 modified "2023-10-14" @default.
- W2040396846 title "A distribution-free geometric upper bound for the probability of error of a minimum distance classifier" @default.
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- W2040396846 doi "https://doi.org/10.1016/0031-3203(78)90037-7" @default.
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