… the item-based k-Nearest Neighbors approach are less diverse than those from the approach
of … As shown in figure 3.2 and be introduced in chapter2, kNN is a non-parametric, lazy …

… method consists of two different recognition techniques, 1) the histogram dissimilarity
measurement with K-Nearest-Neighbour … feature with statistical approach classification. Because …

… : k nearest neighbor (kNN) based method, instead of ignoring missing observations to make
data incomplete. KNN based … We compare the novel imputation techniques with mean …

… (αi,βi,gij,hij) from experimental timeseries data of gene expression. … neighbor algorithm, we
obtained the so-called knearest … [13] as a nonparametric feature pre-selection method. In this …

… Tissue classification was performed using the supervised k-nearest neighbor classifier
for … of infarcts and volume of white matter lesions were determined as non-parametric data. …

… , with k-nearest neighbor consensus and Wang method, using 80% … To generate time series
data and determine the temporal … level) was evaluated based on non-parametric Shannon’s …

… In contrast, k-nearest neighbor (k-NN) models are non-parametric classification algorithms
that … Researchers intend to develop DL classification models based on time series to learn …

… Instead of using raw time series data, we used wavelet … The algorithm calculates the k-nearest
neighbors and … In this section, we used a non-parametric wavelet approach instead of a …

… methods are classified into parametric and non-parametric … 预测的多层k-NN(multilayer
k-nearest neighbor,简称Mk-NN)框架[… Combining kohonen maps with arima time series models to …

… This study used the technique of Non-parametric Nonuniform intensity … the knearest neighbor
(kNN) search method (k=9) as the classifier. To fairly evaluate the accuracy of classification, …