Description: steps:
In order to determine the unknown instance categories, with examples of all known categories as reference
Parameter selection of K
The calculation examples and all known examples of the unknown distance
Choose the closest known instance K
According to the majority voting rule (majority-voting), for example the unknown classified as K most adjacent to the majority in the sample category
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File list (Check if you may need any files):
02KNN\4.1 最邻近规则分类(K-Nearest Neighbor)KNN算法.html
02KNN\4.1 最邻近规则分类(K-Nearest Neighbor)KNN算法_files\Image [1].png
02KNN\4.1 最邻近规则分类(K-Nearest Neighbor)KNN算法_files\Image [2].png
02KNN\4.1 最邻近规则分类(K-Nearest Neighbor)KNN算法_files\Image [3].png
02KNN\4.1 最邻近规则分类(K-Nearest Neighbor)KNN算法_files\Image [4].png
02KNN\4.1 最邻近规则分类(K-Nearest Neighbor)KNN算法_files\Image.png
02KNN\4.1 最邻近规则分类(K-Nearest Neighbor)KNN算法_files\images.jpg
02KNN\4.1 最邻近规则分类(K-Nearest Neighbor)KNN算法_files\imgres.png
02KNN\iris.py
02KNN\irisdata.txt
02KNN\irisdata.txt~
02KNN\KNN.py
02KNN\KNNImplementation (my).py
02KNN\KNNImplementation (myfix).py
02KNN\KNNImplementation.py
02KNN\4.1 最邻近规则分类(K-Nearest Neighbor)KNN算法_files
02KNN
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