{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T12:42:10Z","timestamp":1766580130321,"version":"3.32.0"},"reference-count":9,"publisher":"Walter de Gruyter GmbH","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>We compare different <jats:italic>machine learning<\/jats:italic> estimators and present details about their implementation in Python.\nThe computational studies are conducted for <jats:italic>classification<\/jats:italic> as well as <jats:italic>regression<\/jats:italic> problems.\nMoreover, as one of the founding problems of machine learning, we present the specific <jats:italic>classification<\/jats:italic> task of <jats:italic>handwritten digit recognition<\/jats:italic>.\nIn this connection, we discuss the mathematical formulation and of course the implementation details of this problem.\nAll corresponding Python code is fully provided on request and can be downloaded from the author\u2019s GitHub page <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/Fab1Fatal\">https:\/\/github.com\/Fab1Fatal<\/jats:ext-link>.<\/jats:p>","DOI":"10.1515\/cmam-2023-0198","type":"journal-article","created":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T18:50:59Z","timestamp":1706122259000},"page":"153-171","source":"Crossref","is-referenced-by-count":1,"title":["Machine Learning Estimators: Implementation and Comparison in Python"],"prefix":"10.1515","volume":"25","author":[{"given":"Fabian","family":"Merle","sequence":"first","affiliation":[{"name":"Mathematisches Institut , Universit\u00e4t T\u00fcbingen , Auf der Morgenstelle 10, 72076 T\u00fcbingen , Germany"}]}],"member":"374","published-online":{"date-parts":[[2024,1,25]]},"reference":[{"key":"2025010318340216571_j_cmam-2023-0198_ref_001","doi-asserted-by":"crossref","unstructured":"L. Devroye, L. Gy\u00f6rfi and G. Lugosi,\nA Probabilistic Theory of Pattern Recognition,\nAppl. Math. (New York) 31,\nSpringer, New York, 1996.","DOI":"10.1007\/978-1-4612-0711-5"},{"key":"2025010318340216571_j_cmam-2023-0198_ref_002","doi-asserted-by":"crossref","unstructured":"T. Dunst and A. Prohl,\nThe forward-backward stochastic heat equation: Numerical analysis and simulation,\nSIAM J. Sci. Comput. 38 (2016), 10.1137\/15M1022951.","DOI":"10.1137\/15M1022951"},{"key":"2025010318340216571_j_cmam-2023-0198_ref_003","doi-asserted-by":"crossref","unstructured":"L. Gy\u00f6rfi, M. Kohler, A. Krzyzak and H. Walk,\nA Distribution-Free Theory of Nonparametric Regression,\nSpringer Ser. Statist.,\nSpringer, New York, 2002.","DOI":"10.1007\/b97848"},{"key":"2025010318340216571_j_cmam-2023-0198_ref_004","doi-asserted-by":"crossref","unstructured":"C. F. Higham and D. J. Higham,\nDeep learning: An Introduction for applied mathematicians,\nSIAM Rev. 61 (2019), 10.1137\/18M1165748.","DOI":"10.1137\/18M1165748"},{"key":"2025010318340216571_j_cmam-2023-0198_ref_005","unstructured":"I. Steinwart and A. Christmann,\nSupport Vector Machines,\nInform. Sci. Statist.,\nSpringer, New York, 2008."},{"key":"2025010318340216571_j_cmam-2023-0198_ref_006","unstructured":"https:\/\/en.wikipedia.org\/wiki\/Normalization_(image_processing)."},{"key":"2025010318340216571_j_cmam-2023-0198_ref_007","unstructured":"https:\/\/en.wikipedia.org\/wiki\/Spatial_anti-aliasing."},{"key":"2025010318340216571_j_cmam-2023-0198_ref_008","unstructured":"https:\/\/github.com\/Fab1Fatal."},{"key":"2025010318340216571_j_cmam-2023-0198_ref_009","unstructured":"https:\/\/scikit-learn.org\/stable\/."}],"container-title":["Computational Methods in Applied Mathematics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/cmam-2023-0198\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/cmam-2023-0198\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T18:34:27Z","timestamp":1735929267000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/cmam-2023-0198\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,25]]},"references-count":9,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,4,13]]},"published-print":{"date-parts":[[2025,1,1]]}},"alternative-id":["10.1515\/cmam-2023-0198"],"URL":"https:\/\/doi.org\/10.1515\/cmam-2023-0198","relation":{},"ISSN":["1609-4840","1609-9389"],"issn-type":[{"type":"print","value":"1609-4840"},{"type":"electronic","value":"1609-9389"}],"subject":[],"published":{"date-parts":[[2024,1,25]]}}}