Abstract: Optimizing the K value in the K-Nearest Neighbor (KNN) algorithm is a critical step in enhancing model performance, particularly for tasks related to classification and prediction. The Elbow ...

1 School of Computer Science and Technology, Yibin University, Yibin, China 2 School of Computer and Software, Southwest Petroleum University, Chengdu, China Ever since Density Peak Clustering (DPC) ...

I feel trapped: afraid of overstepping with unpredictable neighbors, afraid of doing nothing and regretting it. By Kwame Anthony Appiah Kwame Anthony Appiah has been the The New York Times Magazine’s ...

Dr. James McCaffrey presents a complete end-to-end demonstration of k-nearest neighbors regression using JavaScript. There are many machine learning regression techniques, but k-nearest neighbors is ...

1 Yibin University, School of Computer Science and Technology, Yibin, China 2 Southwest Petroleum University, School of Computer and Software, Chengdu, China Network security is the core guarantee for ...

Contrastive image and text models face significant challenges in optimizing retrieval accuracy despite their crucial role in large-scale text-to-image and image-to-text retrieval systems. While these ...

Dr. James McCaffrey of Microsoft Research presents a full demo of k-nearest neighbors classification on mixed numeric and categorical data. Compared to other classification techniques, k-NN is easy to ...

This repository contains a Python implementation of a K-Nearest Neighbors (KNN) classifier from scratch. It's applied to the "BankNote_Authentication" dataset, which consists of four features ...

Source codes and datasets used for the undergraduate capstone project entitled "Machine Learning Algorithms for the Detection of GPS Spoofing in Intelligent Transportation Systems" ...