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- W1587400186 abstract "In order to classify an unseen (query) vector qwith the k-Nearest Neighbors method (k-NN) one computes a similarity function between qand training vectors in a database. In the basic variant of the k-NN algorithm the predicted class of qis estimated by taking the majority class of the q's k-nearest neighbors. Various similarity functions may be applied leading to different classification results. In this paper a heterogeneous similarity function is constructed out of different 1-component metrics by minimization of the number of classification errors the system makes on a training set. The HSFL-NN system, which has been introduced in this paper, on five tested datasets has given better results on unseen samples than the plain k-NN method with the optimally selected kparameter and the optimal homogeneous similarity function." @default.
- W1587400186 created "2016-06-24" @default.
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- W1587400186 date "2008-08-12" @default.
- W1587400186 modified "2023-09-26" @default.
- W1587400186 title "Towards Heterogeneous Similarity Function Learning for the k-Nearest Neighbors Classification" @default.
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- W1587400186 doi "https://doi.org/10.1007/978-3-540-69731-2_56" @default.
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