{"id":7908,"date":"2020-12-17T03:52:23","date_gmt":"2020-12-17T03:52:23","guid":{"rendered":"https:\/\/dataaspirant.com\/?p=7908"},"modified":"2023-10-23T08:49:20","modified_gmt":"2023-10-23T03:19:20","slug":"svm-kernels","status":"publish","type":"post","link":"https:\/\/dataaspirant.com\/svm-kernels\/","title":{"rendered":"Seven Most Popular SVM Kernels"},"content":{"rendered":"
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While explaining the support vector machine<\/strong><\/a>, SVM algorithm, we said we have various svm kernel functions that help changing the data dimensions.<\/p>\n

So In this article, we are going to dive deep into svm algorithm and SVM\u2019s kernel functions.<\/p>\n

Let me give you a quick introduction of svm<\/strong><\/a> and its kernels. <\/p><\/blockquote>\n

We already know that SVM is a supervised machine learning algorithm used to deal with both classification and regression problems<\/strong><\/a>. Compared to the other classification and regression algorithms, the svm approach is completely different. <\/p>\n

One key reason for this is svm kernel<\/strong> functions.<\/p>\n

Kernel plays a vital role in classification and is used to analyze some patterns in the given dataset. They are very helpful in solving a no-linear problem<\/strong> by using a linear classifier<\/strong><\/a>. <\/p>\n

Later the svm algorithm uses kernel-trick<\/strong> for transforming the data points and creating an optimal decision boundary. Kernels help us to deal with high dimensional<\/strong> data in a very efficient manner. <\/p>\n

We have various svm kernel functions to convert the non-linear data to linear. In this article, we listed 7 such popular svm kernel functions.<\/p>\n

Before we drive further, let\u2019s have a look at the topics you are going to learn in this article.<\/p>\n

Let\u2019s start the article with SVM. If you are interested in the sum algorithm implementation in python and R programming language, please refer to below two articles.<\/p>\n