SDKNN k-nearest neighbor classifier P=SDKNN(DATA,options) P=SDKNN(DATA,K) INPUT DATA training dataset K neighborhood size (def: k=1) OPTIONS k number of neighbors (def: 1) proto number of prototypes to select per class (def: [] = use all samples) protosel prototype selection method (def: 'random') 'kcentres','random' method method to compute k-NN with k>1 (def: 'distance') 'distance' - outputs per-class distance to k-th neighbor (one/multi class) 'classfrac' - outputs class fraction between k neighbors (only multi class) OUTPUT P pipeline object DESCRIPTION SDKNN trains a k-NN classifier using Euclidean distance. By default, k=1. SDKNN may be trained on one class - by default it will accept all training examples. By default all provided examples are used as prototypes. Prototype selection may be performed by setting number of desired prototypes using the 'proto' option. READ MORE http://perclass.com/doc/guide/classifiers.html#sdknn SEE ALSO SDKMEANS, SDKCENTRES, SDPARZEN
sdknn
is referenced in examples: