Abstract: The article presents a Local Weighted Mean-Based k-Nearest Neighbor ... on the distance between samples, which enables it to better capture the complex structure of the data. The article ...

Development of Machine Learning Algorithms Incorporating Electronic Health Record ... studies of patient cohorts across multiple omics platforms. Holistic analysis of these data can generate ...

Nearest neighbor method ... catalog containing data on clustered seismicity, e.g., earthquake swarms, which are an important feature of the seismicity (Ross et al., 2020). The automated procedure ...

In this paper, we propose a hybrid heuristic algorithm to solve the symmetric TSP problem by combining the search mechanism of repetitive nearest neighbor ... clustering of data arrays, image ...

To classify a new sample, its low-dimensional features were obtained as the weighted mean vector of its nearest neighbors in the training set. Using the low-dimensional features as input, three ...

Machine learning is a branch of artificial intelligence that includes methods, or algorithms, for automatically creating models from data ... Regression, K-Nearest Neighbors, and Support Vector ...

Wu and Govindaraju in 2004 [5] , Enhancement algorithm based on Fourier analysis that is ... Using KNN Neural Network K-nearest neighbor classification is the simplest technique in machine learning, ...