{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-simple-squared-error-reformulation-for","title":"A simple squared-error reformulation for ordinal classification","arxiv_id":"1612.00775","date":"2016-12-02","proceeding":null,"authors":["Christopher Beckham","Christopher Pal"],"abstract":"In this paper, we explore ordinal classification (in the context of deep\nneural networks) through a simple modification of the squared error loss which\nnot only allows it to not only be sensitive to class ordering, but also allows\nthe possibility of having a discrete probability distribution over the classes.\nOur formulation is based on the use of a softmax hidden layer, which has\nreceived relatively little attention in the literature. We empirically evaluate\nits performance on the Kaggle diabetic retinopathy dataset, an ordinal and\nhigh-resolution dataset and show that it outperforms all of the baselines\nemployed.","url_abs":"http://arxiv.org/abs/1612.00775v2","url_pdf":"http://arxiv.org/pdf/1612.00775v2.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":"a-simple-squared-error-reformulation-for","repo_url":"https://github.com/rasbt/deeplearning-models","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"ordinal-classification","task_name":"Ordinal Classification"}],"methods":[{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1612.00775","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}