Apr 1, 2012 · In this paper, new algorithms to reduce the size of the training set for use in a classification task have been presented. These algorithms give ...
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Apr 1, 2012 · The Condensed Nearest Neighbor Rule (CNN) (Hart, 1968) was one of the first techniques to reduce the size of the training set. This algorithm ...
New rank methods for reducing the size of the training set using the nearest neighbor rule ... Abstract. Some new rank methods to select the best prototypes from ...
... The first sample reduction algorithm was proposed by Hart in the condensed nearest neighbor (CNN) rule [16], which finds a subset X sub tr of the training ...
Some new rank methods to select the best prototypes from a training set are proposed in this paper in order to establish its size according to an external ...
Bibliographic details on New rank methods for reducing the size of the training set using the nearest neighbor rule.
New rank methods for reducing the size of the training set using the nearest neighbor rule. Rico-Juan, J.R. · Iñesta, J.M.. Revista: Pattern Recognition Letters.
Corrigendum: Corrigendum to "New rank methods for reducing the size of the training set using the nearest neighbor rule" [Pattern Recognition Lett.
This guide to the K-Nearest Neighbors (KNN) algorithm in machine learning provides the most recent insights and techniques.
PDF | The k-nearest neighbor (KNN) rule is a successful technique in pattern classification due to its simplicity and effectiveness. As a supervised.