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Data Mining Notes Technical Chapter-Chapter5 Classification Algorithm KNN
Build a classifier based on relative distance measurements Among them, the most representative is the KNN algorithm Reference: "Principles of Statistical Learning Li Hang" 1. Introduction to KNN algorithm data set: T=[(x1,y1),(x2,y2),...,(xn,yn)]T={[(x_1,y_1),(x_2,y_2),...,(x_n,y_n)]}T=[ ( x1,y1) ,( x2,y2) ,. . . ,( xn,yn) ] among them xiAXARnx_i \in \mathcal{X} \in \mathbb{R}^nxiAXARn is the feature vector of the instance, yiAY=[c1,c2,...,cN]y_i \in \mathcal{Y}=[c_1,c_2,...,c_N]yiAY=[ c1,c2,. .