{"id":3700,"date":"2017-08-12T12:29:07","date_gmt":"2017-08-12T06:59:07","guid":{"rendered":"http:\/\/data-flair.training\/blogs\/?p=3700"},"modified":"2025-07-28T15:27:20","modified_gmt":"2025-07-28T09:57:20","slug":"svm-kernel-functions","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/svm-kernel-functions\/","title":{"rendered":"Kernel Functions-Introduction to SVM Kernel &amp; Examples"},"content":{"rendered":"<div class='__iawmlf-post-loop-links' style='display:none;' data-iawmlf-post-links='[{&quot;id&quot;:1418,&quot;href&quot;:&quot;https:\\\/\\\/en.wikipedia.org\\\/wiki\\\/Machine_learning&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20251130072921\\\/https:\\\/\\\/en.wikipedia.org\\\/wiki\\\/Machine_learning&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-09 06:41:40&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-12 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13:13:21&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-27 13:51:19&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-30 15:11:03&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-02 18:48:44&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-06-06 01:41:18&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-09 05:37:43&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-06-12 11:02:59&quot;,&quot;http_code&quot;:200}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-06-12 11:02:59&quot;,&quot;http_code&quot;:200},&quot;process&quot;:&quot;done&quot;}]'><\/div>\n<p>In our previous <a href=\"http:\/\/data-flair.training\/blogs\/machine-learning-tutorial\/\"><strong>Machine Learning<\/strong><\/a> blog we have discussed about <a href=\"http:\/\/data-flair.training\/blogs\/svm-support-vector-machine-tutorial\/\"><strong>SVM (Support Vector Machine)<\/strong><\/a> in Machine Learning. Now we are going to provide you a detailed description of SVM Kernel and Different Kernel Functions and its examples such as linear, nonlinear, polynomial, Gaussian kernel, Radial basis function (RBF), sigmoid etc.<\/p>\n<h3>SVM Kernel Functions<\/h3>\n<p>SVM algorithms use a set of mathematical functions that are defined as the kernel. The function of kernel is to take data as input and transform it into the required form. Different SVM algorithms use different types of kernel functions. These functions can be different types. For example<strong><em> linear, nonlinear, polynomial, radial basis function (RBF), and sigmoid.<\/em><\/strong><\/p>\n<p>Introduce Kernel functions for sequence data, graphs, text, images, as well as vectors. The most used type of kernel function is <strong>RBF.<\/strong> Because it has localized and finite response along the entire x-axis.<\/p>\n<p>The kernel functions return the inner product between two points in a suitable feature space. Thus by defining a notion of similarity, with little computational cost even in very high-dimensional spaces.<\/p>\n<p>Actually, the selection of appropriate kernel function is one of the critical factors affecting the SVM model. The linear kernel is best used for linearly separable data while the polynomial kernel should be used for the data which have polynomial related structures. Which is very flexible and suitable for use most of the time particularly when the data is not separable in the original coordinate axes.<\/p>\n<p>Also, kernel functions help SVMs to function optimally in high-dimensional space while at the same avoiding the computation of high-dimensional data space coordinates. Due to this ability of mapping the inputs into the higher dimensional feature spaces the SVMs can be used effectively in various machine learning techniques such as classification, regression and outlier detection.<\/p>\n<h3>Kernel Rules<\/h3>\n<p>Define kernel or a window function as follows:<\/p>\n<div id=\"attachment_3704\" style=\"width: 310px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/kernal-rule.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-3704\" class=\"wp-image-3704 size-medium\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/kernal-rule-300x92.png\" alt=\"Kernel or a window function\" width=\"300\" height=\"92\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/kernal-rule-300x92.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/kernal-rule-150x46.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/kernal-rule.png 516w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/a><p id=\"caption-attachment-3704\" class=\"wp-caption-text\">Kernel or a window function<\/p><\/div>\n<p>This value of this function is 1 inside the closed ball of radius 1 centered at the origin, and 0 otherwise . As shown in the figure below:<\/p>\n<div id=\"attachment_3705\" style=\"width: 798px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/kernal-rule-graph-1.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-3705\" class=\"wp-image-3705 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/kernal-rule-graph-1.png\" alt=\"Kernel or a window function\" width=\"788\" height=\"423\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/kernal-rule-graph-1.png 788w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/kernal-rule-graph-1-150x81.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/kernal-rule-graph-1-300x161.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/kernal-rule-graph-1-768x412.png 768w\" sizes=\"auto, (max-width: 788px) 100vw, 788px\" \/><\/a><p id=\"caption-attachment-3705\" class=\"wp-caption-text\">Kernel or a window function<\/p><\/div>\n<p>For a fixed xi, the function is K(z-xi)\/h) = 1 inside the closed ball of radius h centered at xi, and 0 otherwise as shown in the figure below:<\/p>\n<div id=\"attachment_3706\" style=\"width: 454px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/kernal-rule-graph-2.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-3706\" class=\"wp-image-3706 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/kernal-rule-graph-2.png\" alt=\"Kernel or a window function\" width=\"444\" height=\"190\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/kernal-rule-graph-2.png 444w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/kernal-rule-graph-2-150x64.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/kernal-rule-graph-2-300x128.png 300w\" sizes=\"auto, (max-width: 444px) 100vw, 444px\" \/><\/a><p id=\"caption-attachment-3706\" class=\"wp-caption-text\">Kernel or a window function<\/p><\/div>\n<p>So, by choosing the argument of K(\u00b7), you have moved the window to be centered at the point xi and to be of radius h.<\/p>\n<h3>Examples of SVM Kernels<\/h3>\n<p>Let us see some common kernels used with SVMs and their uses:<\/p>\n<h4>1. Polynomial kernel<\/h4>\n<p>It is popular in image processing.<br \/>\nEquation is:<\/p>\n<div id=\"attachment_3707\" style=\"width: 211px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/polynomial-kernel.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-3707\" class=\"wp-image-3707 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/polynomial-kernel.png\" alt=\"Polynomial kernel equation\" width=\"201\" height=\"24\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/polynomial-kernel.png 201w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/polynomial-kernel-150x18.png 150w\" sizes=\"auto, (max-width: 201px) 100vw, 201px\" \/><\/a><p id=\"caption-attachment-3707\" class=\"wp-caption-text\">Polynomial kernel equation<\/p><\/div>\n<p>where d is the degree of the polynomial.<\/p>\n<h4>2. Gaussian kernel<\/h4>\n<p>It is a general-purpose kernel; used when there is no prior knowledge about the data. Equation is:<\/p>\n<div id=\"attachment_3708\" style=\"width: 224px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/gaussian-kernel.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-3708\" class=\"wp-image-3708 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/gaussian-kernel.png\" alt=\"Gaussian kernel equation\" width=\"214\" height=\"46\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/gaussian-kernel.png 214w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/gaussian-kernel-150x32.png 150w\" sizes=\"auto, (max-width: 214px) 100vw, 214px\" \/><\/a><p id=\"caption-attachment-3708\" class=\"wp-caption-text\">Gaussian kernel equation<\/p><\/div>\n<h4>3. Gaussian radial basis function (RBF)<\/h4>\n<p>It is a general-purpose kernel; used when there is no prior knowledge about the data.<br \/>\nEquation is:<\/p>\n<div class=\"mceTemp\"><\/div>\n<div id=\"attachment_3709\" style=\"width: 262px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/gaussian-radial-basis-function-RBF.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-3709\" class=\"wp-image-3709 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/gaussian-radial-basis-function-RBF.png\" alt=\"Gaussian radial basis function (RBF)\" width=\"252\" height=\"24\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/gaussian-radial-basis-function-RBF.png 252w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/gaussian-radial-basis-function-RBF-150x14.png 150w\" sizes=\"auto, (max-width: 252px) 100vw, 252px\" \/><\/a><p id=\"caption-attachment-3709\" class=\"wp-caption-text\">Gaussian radial basis function (RBF)<\/p><\/div>\n<p>, for:<\/p>\n<div id=\"attachment_3710\" style=\"width: 56px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/gaussian-radial-basis-function-RBF-1.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-3710\" class=\"wp-image-3710 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/gaussian-radial-basis-function-RBF-1.png\" alt=\"Gaussian radial basis function (RBF)\" width=\"46\" height=\"18\" \/><\/a><p id=\"caption-attachment-3710\" class=\"wp-caption-text\">Gaussian radial basis function (RBF)<\/p><\/div>\n<p>Sometimes parametrized using:<\/p>\n<div id=\"attachment_3711\" style=\"width: 96px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/gaussian-radial-basis-function-RBF-2.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-3711\" class=\"wp-image-3711 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/gaussian-radial-basis-function-RBF-2.png\" alt=\"Gaussian radial basis function (RBF)\" width=\"86\" height=\"23\" \/><\/a><p id=\"caption-attachment-3711\" class=\"wp-caption-text\">Gaussian radial basis function (RBF)<\/p><\/div>\n<h4>4. Laplace RBF kernel<\/h4>\n<p>It is general-purpose kernel; used when there is no prior knowledge about the data.<br \/>\nEquation is:<\/p>\n<div id=\"attachment_3712\" style=\"width: 216px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/laplace-RBF-kernel.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-3712\" class=\"wp-image-3712 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/laplace-RBF-kernel.png\" alt=\"Laplace RBF kernel equation\" width=\"206\" height=\"45\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/laplace-RBF-kernel.png 206w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/laplace-RBF-kernel-150x33.png 150w\" sizes=\"auto, (max-width: 206px) 100vw, 206px\" \/><\/a><p id=\"caption-attachment-3712\" class=\"wp-caption-text\">Laplace RBF kernel equation<\/p><\/div>\n<h4>5. Hyperbolic tangent kernel<\/h4>\n<p>We can use it in neural networks.<br \/>\nEquation is:<\/p>\n<div id=\"attachment_3713\" style=\"width: 250px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/hyperbolic-tangent-kernel.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-3713\" class=\"wp-image-3713 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/hyperbolic-tangent-kernel.png\" alt=\"Hyperbolic tangent kernel equation\" width=\"240\" height=\"22\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/hyperbolic-tangent-kernel.png 240w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/hyperbolic-tangent-kernel-150x14.png 150w\" sizes=\"auto, (max-width: 240px) 100vw, 240px\" \/><\/a><p id=\"caption-attachment-3713\" class=\"wp-caption-text\">Hyperbolic tangent kernel equation<\/p><\/div>\n<p>, for some (not every) k&gt;0 and c&lt;0.<\/p>\n<h4>6. Sigmoid kernel<\/h4>\n<p>We can use it as the proxy for neural networks. Equation is<\/p>\n<div id=\"attachment_3716\" style=\"width: 204px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/sigmoid-kernel.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-3716\" class=\"wp-image-3716 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/sigmoid-kernel.png\" alt=\" Sigmoid kernel equation\" width=\"194\" height=\"20\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/sigmoid-kernel.png 194w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/sigmoid-kernel-150x15.png 150w\" sizes=\"auto, (max-width: 194px) 100vw, 194px\" \/><\/a><p id=\"caption-attachment-3716\" class=\"wp-caption-text\">Sigmoid kernel equation<\/p><\/div>\n<h4>7. Bessel function of the first kind Kernel<\/h4>\n<p>We can use it to remove the cross term in mathematical functions. Equation is :<\/p>\n<div id=\"attachment_3717\" style=\"width: 205px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/bessel-function.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-3717\" class=\"wp-image-3717 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/bessel-function.png\" alt=\"Equation of Bessel function of the first kind kernel\" width=\"195\" height=\"44\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/bessel-function.png 195w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/bessel-function-150x34.png 150w\" sizes=\"auto, (max-width: 195px) 100vw, 195px\" \/><\/a><p id=\"caption-attachment-3717\" class=\"wp-caption-text\">Equation of Bessel function of the first kind kernel<\/p><\/div>\n<p>where j is the Bessel function of first kind.<\/p>\n<h4>8. ANOVA radial basis kernel<\/h4>\n<p>We can use it in regression problems. Equation is:<\/p>\n<div id=\"attachment_3718\" style=\"width: 266px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/ANOVA-radial-basis-kernel.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-3718\" class=\"wp-image-3718 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/ANOVA-radial-basis-kernel.png\" alt=\"ANOVA radial basis kernel equation\" width=\"256\" height=\"52\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/ANOVA-radial-basis-kernel.png 256w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/ANOVA-radial-basis-kernel-150x30.png 150w\" sizes=\"auto, (max-width: 256px) 100vw, 256px\" \/><\/a><p id=\"caption-attachment-3718\" class=\"wp-caption-text\">ANOVA radial basis kernel equation<\/p><\/div>\n<h4>9. Linear splines kernel in one-dimension<\/h4>\n<p>It is useful when dealing with large sparse data vectors. It is often used in text categorization. The splines kernel also performs well in regression problems. Equation is:<\/p>\n<div id=\"attachment_3719\" style=\"width: 526px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/linear-splines-kernel-in-one-dimension.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-3719\" class=\"wp-image-3719 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/linear-splines-kernel-in-one-dimension.png\" alt=\"Linear splines kernel equation in one-dimension\" width=\"516\" height=\"37\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/linear-splines-kernel-in-one-dimension.png 516w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/linear-splines-kernel-in-one-dimension-150x11.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/08\/linear-splines-kernel-in-one-dimension-300x22.png 300w\" sizes=\"auto, (max-width: 516px) 100vw, 516px\" \/><\/a><p id=\"caption-attachment-3719\" class=\"wp-caption-text\">Linear splines kernel equation in one-dimension<\/p><\/div>\n<h3>Summary<\/h3>\n<p>SVM kernel functions help the model learn even when the data isn\u2019t linearly separated. They are like magic tools that change how the data looks, so it becomes easier to divide. A linear kernel is used when the data can be split by a straight line. It\u2019s fast and works well when the number of features is more than the number of samples.<\/p>\n<p>For more complex data, we use non-linear kernels. These kernels change the space into a higher dimension where the separation becomes possible.<\/p>\n<p>If you have any query about SVM Kernel Functions, So feel free to share with us. We will be glad to solve your queries.<\/p>\n<p><strong>See Also-<\/strong><\/p>\n<ul>\n<li><a href=\"http:\/\/data-flair.training\/blogs\/applications-of-svm\/\">Applications of Support vector Machine (SVM)<\/a><\/li>\n<li><a href=\"http:\/\/data-flair.training\/blogs\/artificial-neural-network-applications\/\">Applications of Artificial Neural Network (ANN)<\/a><\/li>\n<\/ul>\n<p><strong>Reference &#8211; <a href=\"https:\/\/en.wikipedia.org\/wiki\/Machine_learning\">Machine Learning<\/a><\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In our previous Machine Learning blog we have discussed about SVM (Support Vector Machine) in Machine Learning. Now we are going to provide you a detailed description of SVM Kernel and Different Kernel Functions&#46;&#46;&#46;<\/p>\n","protected":false},"author":5,"featured_media":42511,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[36],"tags":[8006,8007,8008,8431,14000,14004,14005,14006],"class_list":["post-3700","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-machine-learning","tag-kernel-function","tag-kernel-functions-examples","tag-kernel-methods","tag-machine-learning","tag-svm","tag-svm-kernel","tag-svm-kernel-functions","tag-svm-kernel-tricks"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Kernel Functions-Introduction to SVM Kernel &amp; Examples - DataFlair<\/title>\n<meta name=\"description\" content=\"Introduction to SVM Kernel &amp; Kernel Functions-Polynomial,Gaussian,RBF,Laplace RBF,Hyperbolic tangent kernel,Bessel function,ANOVA radial basis,Linear spline\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/svm-kernel-functions\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Kernel Functions-Introduction to SVM Kernel &amp; 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