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The k-Nearest Neighbors Classifiers
Published in Mohssen Mohammed, Muhammad Badruddin Khan, Eihab Bashier Mohammed Bashier, Machine Learning, 2016
Mohssen Mohammed, Muhammad Badruddin Khan, Eihab Bashier Mohammed Bashier
In pattern recognition, the k-nearest neighbors algorithm (or k-NN for short) is a nonparametric method used for classification and regression [1]. In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression: In k-NN classification, the output is a class membership. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k-NN (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor.In k-NN regression, the output is the property value for the object. This value is the average of the values of its k-NN.
K
Published in Philip A. Laplante, Comprehensive Dictionary of Electrical Engineering, 2018
K-nearest neighbor algorithm a method of classifying samples in which a sample is assigned to the class which is most represented among the k nearest neighbors; an extension of the nearest neighbor algorithm. Kaczmarz's algorithm the recursive leastsquares algorithm has two sets of state variables, parameters vector and covariance matrix, which must be updated at each step. For large dimension, the updating of the covariance matrix dominates the computing effort. Kaczmarz's projection algorithm is one simple solution that avoids updating the matrix at the cost of slower convergence. The updating formula of the least squares algorithm using the Kaczmarz's algorithm has the form ^ ^ (t) = (t-1)+ (t) ^ (y(t)- T (t)(t-1)) T (t)(t)
Machine learning model for predicting blast fume dilution time in underground working areas
Published in CIM Journal, 2023
A. Adhikari, P. Tukkaraja, S. J. Sridharan, A. Verburg
The K-nearest neighbor algorithm finds the closest data points (neighbors) based on distance metrics such as Euclidean, Manhattan, or Hamming (Figure 4). The predicted value for the test data point is then calculated by averaging the values of its closest neighbors. The advantage of this technique is its simplicity and intuitiveness. Disadvantages include poor performance on imbalanced data and difficulty in choosing the optimal value of K or the number of neighbors to predict the output (Zhang, 2016).
A practical implementation of mask detection for COVID-19 using face detection and histogram of oriented gradients
Published in Australian Journal of Electrical and Electronics Engineering, 2022
Salim Chelbi, Abdenour Mekhmoukh
K-nearest neighbor algorithm (Bremner et al. 2005; Cover and Hart 1967) is a method for classifying objects based on closest training examples in the feature space; k-nearest neighbor algorithm is the one of the simplest machine learning algorithms. The training process for this method consists only of storing feature vectors and labels of the training images.