{"id":1441,"date":"2022-09-01T23:25:43","date_gmt":"2022-09-01T23:25:43","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=1441"},"modified":"2022-09-01T23:25:43","modified_gmt":"2022-09-01T23:25:43","slug":"vol19-no1-art5","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol19-no1\/vol19-no1-art5\/","title":{"rendered":"From MSE to Correntropy, a friendly survey"},"content":{"rendered":"<p>Allan de Medeiros Martins <a href=\"https:\/\/orcid.org\/0000-0002-9486-4509\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a><\/p>\n<p><strong>Abstract:<\/strong> Correntropy is a metric that has been widely used in place of the root mean square error in problems where it is intended to minimize the divergence between data and models. In particular, machine learning is currently in focus, where increasingly complex models require increasingly statistically heterogeneous data. In this article we will give an introduction to correntropy in a friendly and intuitive way. Contrary to purely technical summaries, we will try to balance a precisely technical language with a freer and more informal text. We will present the history of how correntropy came to be developed, in order to lead the reader to a coherent temporal sequence that will facilitate the precise understanding of this new metric.<\/p>\n<p><strong>Keywords:<\/strong> Correntropy, MSE, regression, entropy, information potential<\/p>\n<p><strong>DOI code:<\/strong> <a href=\"http:\/\/dx.doi.org\/10.21528\/lnlm-vol19-no1-art5\">10.21528\/lnlm-vol19-no1-art5<\/a><\/p>\n<p><strong>PDF file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/12\/vol19-no1-art5.pdf\">vol19-no1-art5.pdf<\/a><\/p>\n<p><strong>BibTex file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/12\/vol19-no1-art5.bib\">vol19-no1-art5.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Allan de Medeiros Martins Abstract: Correntropy is a metric that has been widely used in place of the root mean square error in problems where it is intended to minimize the divergence between data and models. In particular, machine learning <a href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol19-no1\/vol19-no1-art5\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":1380,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1441","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>From MSE to Correntropy, a friendly survey - Learning and NonLinear Models<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol19-no1\/vol19-no1-art5\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"From MSE to Correntropy, a friendly survey - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"Allan de Medeiros Martins Abstract: Correntropy is a metric that has been widely used in place of the root mean square error in problems where it is intended to minimize the divergence between data and models. In particular, machine learning Read More ...\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol19-no1\/vol19-no1-art5\/\" \/>\n<meta property=\"og:site_name\" content=\"Learning and NonLinear Models\" \/>\n<meta property=\"og:image\" content=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. tempo de leitura\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minuto\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol19-no1\/vol19-no1-art5\/\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol19-no1\/vol19-no1-art5\/\",\"name\":\"From MSE to Correntropy, a friendly survey - Learning and NonLinear Models\",\"isPartOf\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol19-no1\/vol19-no1-art5\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol19-no1\/vol19-no1-art5\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\",\"datePublished\":\"2022-09-01T23:25:43+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol19-no1\/vol19-no1-art5\/#breadcrumb\"},\"inLanguage\":\"pt-BR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol19-no1\/vol19-no1-art5\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"pt-BR\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol19-no1\/vol19-no1-art5\/#primaryimage\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\",\"contentUrl\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol19-no1\/vol19-no1-art5\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Browse issues\",\"item\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Learning &#038; Nonlinear Models &#8211; L&#038;NLM &#8211; Volume 19 &#8211; N\u00famero 1\",\"item\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol19-no1\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"From MSE to Correntropy, a friendly survey\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#website\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/\",\"name\":\"Learning and NonLinear Models\",\"description\":\"\",\"publisher\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/sbia.org.br\/lnlm\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"pt-BR\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#organization\",\"name\":\"Learning and NonLinear Models\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"pt-BR\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/07\/logo-lnlm.png\",\"contentUrl\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/07\/logo-lnlm.png\",\"width\":398,\"height\":94,\"caption\":\"Learning and NonLinear Models\"},\"image\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#\/schema\/logo\/image\/\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"From MSE to Correntropy, a friendly survey - Learning and NonLinear Models","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol19-no1\/vol19-no1-art5\/","og_locale":"pt_BR","og_type":"article","og_title":"From MSE to Correntropy, a friendly survey - Learning and NonLinear Models","og_description":"Allan de Medeiros Martins Abstract: Correntropy is a metric that has been widely used in place of the root mean square error in problems where it is intended to minimize the divergence between data and models. 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