The k nearest neighbor algorithm in pattern recognition has been tested and the result is very good. Minimum distance classifier: it divides all kinds of training samples into several sub classes and identifies the representative points in each sub class. The categories of test samples are made recently by making decisions with these representatives. The disadvantage of this method is that the selected representative points do not necessarily represent the various types well, and the consequences will increase the error rate.

Filename	Size	Update
knn\Knn.cpp	4103	2009-06-30
knn\Knn.cpp.bak	3861	2009-06-30
knn\Knn.h	407	2009-06-29
knn\Knn.h.bak	407	2009-06-29
knn\main.cpp	946	2018-04-30
knn\main.cpp.bak	1166	2009-06-30
knn\testdata.txt	782	2009-06-30
knn\testdata.txt.bak	0	2009-06-30
knn	0	2018-04-30

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