Firstly, a subset of potentially influential data cases is found by constructing the smallest enclosing hypersphere (for each group) in feature space. Secondly, ...

In this paper we therefore propose using the smallest enclosing hypersphere as a pre-processing step to reduce the number of cases to be evaluated by the ...

This paper addresses the problem of classifying multidimensional data with relatively few training samples available. Classification is often performed based on ...

In this paper we propose a two-step procedure for identifying influential cases in large data sets. Firstly, a subset of potentially influential data cases is ...

Identifying Influential Cases in Kernel Fisher Discriminant Analysis by Using the Smallest Enclosing Hypersphere. GfKl 2008: 363-371. [+][–]. Coauthor network.

Lamont, Identification of Influential cases in Kernel Fisher Discriminant Analysis by using the Smallest Enclosing Hypersphere, in Advances in Data Analysis ...

Kernel Fisher discriminant analysis (KFDA) is a kernel-based technique that can be used to classify observations of unknown origin into predefined groups.

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Identifying Influential Cases in Kernel Fisher Discriminant Analysis by Using the Smallest Enclosing Hypersphere. GfKl 2008: 363-371; 2006. [j1]. view.

Identifying Influential Cases in Kernel Fisher Discriminant Analysis by Using the Smallest Enclosing Hypersphere. Conference Paper. Jan 2008.