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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.