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FLAIRS
2004

Prototype Based Classifier Design with Pruning

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Prototype Based Classifier Design with Pruning
An algorithm is proposed to prune the prototype vectors (prototype selection) used in a nearest neighbor classifier so that a compact classifier can be obtained with similar or even better performance. The pruning procedure is error based; a prototype will be pruned if its deletion leads to the smallest classification error increase. Also each pruning iteration is followed by one epoch of Learning Vector Quantization (LVQ) training. Simulation results show that the selected prototypes can approach optimal or near optimal locations based on the training data distribution.
Jiang Li, Michael T. Manry, Changhua Yu
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2004
Where FLAIRS
Authors Jiang Li, Michael T. Manry, Changhua Yu
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