On the minimum error in addition processes of positive floating-point numbers
A. Schurmann (1973)
Applicationes Mathematicae
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A. Schurmann (1973)
Applicationes Mathematicae
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L. Vostrikova (1989-1990)
Publications mathématiques et informatique de Rennes
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Waclaw Timoszyk (1974)
Colloquium Mathematicae
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Li, Ming, Li, Jia-Yue (2010)
Mathematical Problems in Engineering
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Šarūnas Raudys (1998)
Kybernetika
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Small learning-set properties of the Euclidean distance, the Parzen window, the minimum empirical error and the nonlinear single layer perceptron classifiers depend on an “intrinsic dimensionality” of the data, however the Fisher linear discriminant function is sensitive to all dimensions. There is no unique definition of the “intrinsic dimensionality”. The dimensionality of the subspace where the data points are situated is not a sufficient definition of the “intrinsic dimensionality”....
Friedrich Stummel (1985)
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B. Č. Popović (1984)
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Krzysztof Michalak, Halina Kwaśnicka (2006)
International Journal of Applied Mathematics and Computer Science
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In classification problems, the issue of high dimensionality, of data is often considered important. To lower data dimensionality, feature selection methods are often employed. To select a set of features that will span a representation space that is as good as possible for the classification task, one must take into consideration possible interdependencies between the features. As a trade-off between the complexity of the selection process and the quality of the selected feature set,...
Galtier, Thomas, Gupta, Sayan, Rychlik, Igor (2010)
Journal of Probability and Statistics
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