KDE is a non-parametric estimation method which can extract the distribution rule from the data samples and solve the distribution density function of random variables from the given sample set. KNN implements learning based on the k-nearest neighbors of each query point, where k is an integer value specified by the user.
Please cite "A systematic analysis of miRNA markers and classification algorithms for forensic body fluid identification. Yang Liu, Hongxia He, Zhi-Xiong Xiao, Anquan Ji, Jian Ye, Qifan Sun, Yang Cao. , 2020 "
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