Machine learning algorithms help computers analyse large datasets and make accurate predictions automatically. Classic models like regression, decision trees, and KNN remain important in modern AI ...

Abstract: The traditional K-Nearest Neighbor (KNN) algorithm often encounters problems such as weak feature expression ability and poor adaptability to fixed K-values in image classification tasks, ...

Monitoring of natural resources is a major challenge that remote sensing tools help to facilitate. The Sissili province in Burkina Faso is a territory that includes significant areas dedicated for the ...

To understand and implement the K-Nearest Neighbors (KNN) algorithm for solving classification problems using the Iris dataset. This project demonstrates data preprocessing, model training, evaluation ...

ABSTRACT: This study addresses the growing demand for news text classification driven by the rapid expansion of internet information by proposing a classification algorithm based on a Bidirectional ...

Quantum computing has become a breakthrough in many different research and applied areas. As various authors have demonstrated, the quantum properties have made some computational processes parallel ...

Abstract: K-nearest neighbor classification algorithm can quickly deal with the classification problem in this paper, but when calculating the similarity, it will assign the same weight to all ...

Hi, thanks for sharing your great work! I have a concern about the KNN in SCAN training. In the Eq.2 of your paper, you calculate the loss by maximizing the similarities between each anchor and its ...