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k-NN object for PD
Introduction
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.
Feature Weighting
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.
Karl MacMillan <karlmac@peabody.jhu.edu>
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