Abstract: The K-nearest neighbors (kNNs) algorithm, a cornerstone of supervised learning, relies on similarity measures constrained by real-number-based distance metrics. A critical limitation of ...

The idea that we might be living inside a vast computer simulation, much like in The Matrix, has fascinated philosophers and scientists for years. But a new study from researchers at the University of ...

The shared-nearest-neighbor-based clustering by fast search and find of density peaks (SNN-DPC) algorithm was proposed by Liu et al. [24], which redefines local density based on nearest neighbors and ...

ABSTRACT: The objective of this work is to determine the true owner of a land—public or private—in the region of Kumasi (Ghana). For this purpose, we applied different machine learning methods to the ...

Embedding-based search outperforms traditional keyword-based methods across various domains by capturing semantic similarity using dense vector representations and approximate nearest neighbor (ANN) ...

Abstract: Nonintrusive load monitoring (NILM) can effectively evaluate and track fine-grained energy consumption at the appliance level by using instruments or devices such as smart meters, providing ...

Six methods—multi-level perceptron, random forest, K-nearest neighbor, support vector machine, logistic regression, and extreme gradient boosting—were used to build predictive models. Various model ...

This project demonstrates how to implement the K-Nearest Neighbors (KNN) algorithm for classification on a customer dataset. The program iterates through different values of k (number of neighbors) ...

In this paper we compare track data association purity, accuracy, and timing on a simple, idealized model tracking problem for two data association methods: Global Nearest Neighbor (GNN) and Linear ...