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k Nearest Neighbor algorithm

Current release

No stable release available yet.

If you are interested in getting the source code of this project, you can get it from the code repository.

Experimental releases

There are no experimental releases available at the moment.

Project Description

Project resources

The k Nearest Neighbor algorithm is a simple and robust classification
algorithm. It compares an unknown feature vector to a database by
calculating distance and assigns the classification of the k closest
database entries to the unknown vector. This implementation for PD
takes inputs of PD lists for both learning and classification.
Currently the length of the feature vectors and maximum number of
classes is fixed at compile time.

This implementation also includes feature weighting to allow control
over the relative importance of each feature. This has been shown to
provide significant improvements to the performance of the k-NN
algorithm.

by Jamie Bullock last modified 2012-12-08 03:57 AM

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