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- W833422060 abstract "NGE (Nested Generalized Exemplars) proposed by Salzberg improved the storage requirement and classification rate of the Memory Based Reasoning. It constructs hyperrectangles during training and performs classification tasks. It worked not bad in many area, however, the major drawback of NGE is constructing hyperrectangles because its hyperrectangle is extended so as to cover the error data and the way of maintaining the feature weight vector. We proposed the OH (Optimizing Hyperrectangle) algorithm which use the feature weight vectors and the ED(Exemplar Densimeter) to optimize resulting Hyperrectangles. The proposed algorithm, as well as the EACH, required only approximately 40% of memory space that is needed in k-NN classifier, and showed a superior classification performance to the EACH. Also, by reducing the number of stored patterns, it showed excellent results in terms of classification when we compare it to the k-NN and the EACH." @default.
- W833422060 created "2016-06-24" @default.
- W833422060 creator A5061440708 @default.
- W833422060 date "2003-06-01" @default.
- W833422060 modified "2023-09-23" @default.
- W833422060 title "An Optimizing Hyperrectangle method for Nearest Hyperrectangle Learning" @default.
- W833422060 doi "https://doi.org/10.5391/jkiis.2003.13.3.328" @default.
- W833422060 hasPublicationYear "2003" @default.
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