Skip to content
/ Image-Classification-on-the-CIFAR-10-Dataset Public
forked from YoongiKim/CIFAR-10-images

This study investigates the efficacy of the k-nearest neighbors (k-NN) algorithm in classifying grayscale images from the CIFAR-10 dataset. 🚀 #ImageProcessing #KNN

0 stars 43 forks Branches Tags Activity
Star
Notifications You must be signed in to change notification settings

FahimMahi/Image-Classification-on-the-CIFAR-10-Dataset

BranchesTags
 
 

Repository files navigation

Image Classification on the CIFAR-10 Dataset: A Comparative Study of Manhattan and Euclidean Distances With 5-Fold Cross-Validation

This study investigates the efficacy of the k-nearest neighbors (k-NN) algorithm in classifying grayscale images from the CIFAR-10 dataset, focusing on comparing the performance of Manhattan (L1) and Euclidean (L2) distance metrics.

About

This study investigates the efficacy of the k-nearest neighbors (k-NN) algorithm in classifying grayscale images from the CIFAR-10 dataset. 🚀 #ImageProcessing #KNN

Resources

Readme
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 100.0%