1.0Learning and NonLinear Modelshttps://sbia.org.br/lnlmsbicbrhttps://sbia.org.br/lnlm/author/sbicbr/rich600338<blockquote class="wp-embedded-content" data-secret="NkaTkxcVYq"><a href="https://sbia.org.br/lnlm/publicacoes/vol19-no1/vol19-no1-art5/">From MSE to Correntropy, a friendly survey</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://sbia.org.br/lnlm/publicacoes/vol19-no1/vol19-no1-art5/embed/#?secret=NkaTkxcVYq" width="600" height="338" title="“From MSE to Correntropy, a friendly survey” — Learning and NonLinear Models" data-secret="NkaTkxcVYq" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script type="text/javascript"> /* <![CDATA[ */ /*! This file is auto-generated */ !function(d,l){"use strict";l.querySelector&&d.addEventListener&&"undefined"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!/[^a-zA-Z0-9]/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret="'+t.secret+'"]'),o=l.querySelectorAll('blockquote[data-secret="'+t.secret+'"]'),c=new RegExp("^https?:$","i"),i=0;i<o.length;i++)o[i].style.display="none";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute("style"),"height"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):"link"===t.message&&(r=new URL(s.getAttribute("src")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener("message",d.wp.receiveEmbedMessage,!1),l.addEventListener("DOMContentLoaded",function(){for(var e,t,s=l.querySelectorAll("iframe.wp-embedded-content"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute("data-secret"))||(t=Math.random().toString(36).substring(2,12),e.src+="#?secret="+t,e.setAttribute("data-secret",t)),e.contentWindow.postMessage({message:"ready",secret:t},"*")},!1)))}(window,document); //# sourceURL=https://sbia.org.br/lnlm/wp-includes/js/wp-embed.min.js /* ]]> */ </script> 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 ...https://sbia.org.br/lnlm/wp-content/uploads/sites/4/2020/09/orcid.jpg