(New page: The classifiers do not use any model to fit the data and only based on memory. The KNN uses neighborhood classification as the predication value of the new query. It has advantages - nonpa...) |
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Revision as of 23:38, 24 April 2008
The classifiers do not use any model to fit the data and only based on memory. The KNN uses neighborhood classification as the predication value of the new query. It has advantages - nonparametric architecture, simple and powerful, requires no traning time, but it also has disadvantage - memory intensive, classification and estimation are slow. Please refer to KNN tutorial website.
1. KNN Tutorial : Contents are below / How K-Nearest Neighbor (KNN) Algorithm works? / Numerical Example (hand computation) / KNN for Smoothing and Prediction / How do we use the spreadsheet for KNN? / Strength and Weakness of K-Nearest Neighbor Algorithm / Resources for K Nearest Neighbors Algorithm
2. KNN
4. WIKIPEDIA
