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We aim to\nstudy a widely applicable classification problem and apply a simple yet\nefficient K-nearest neighbor classifier with an enhanced heuristic. We evaluate\nthe performance of the K-nearest neighbor classification algorithm on the MNIST\ndataset where the $L2$ Euclidean distance metric is compared to a modified\ndistance metric which utilizes the sliding window technique in order to avoid\nperformance degradation due to slight spatial misalignments. The accuracy\nmetric and confusion matrices are used as the performance indicators to compare\nthe performance of the baseline algorithm versus the enhanced sliding window\nmethod and results show significant improvement using this proposed method.","url_abs":"http://arxiv.org/abs/1809.06846v4","url_pdf":"http://arxiv.org/pdf/1809.06846v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"mnist-dataset-classification-utilizing-k-nn","repo_url":"https://github.com/BehradToghi/kNN_SWin","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.06846","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.06846"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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