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leaning vector quantization and k-nearest neighbor

leaning vector quantization and k-nearest neighbor

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Learning vector quantization is to move a prototype close to training samples in its class and move away from samples with different classes. It uses information given by class lables, and often works better than k-means. For k-nearest neighbor classifiers, classification boundaries become smoother with larger k.
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