{"id":9578,"date":"2026-07-27T12:30:27","date_gmt":"2026-07-27T10:30:27","guid":{"rendered":"https:\/\/www.kubicek.ai\/?post_type=lexicon&#038;p=9578"},"modified":"2026-07-27T13:35:01","modified_gmt":"2026-07-27T11:35:01","slug":"support-vector-machine-svm","status":"publish","type":"lexicon","link":"https:\/\/www.kubicek.ai\/en\/lexicon\/support-vector-machine-svm\/","title":{"rendered":"Support Vector Machine (SVM)"},"content":{"rendered":"<p class=\"wp-block-paragraph\">The <strong>Support Vector Machine (SVM)<\/strong> is a supervised learning algorithm for both classification and regression whose aim is not merely to separate the classes but to separate them with the maximum margin. It therefore looks for the separating hyperplane with the greatest possible distance from the nearest points of both classes; those extreme points are called support vectors and alone determine the resulting solution \u2013 the other samples can be removed without changing the model. The parameter C governs the trade-off between margin width and tolerance of misclassified points, thereby acting as regularisation. The key idea is the kernel trick: instead of explicitly transforming the data into a high-dimensional space where it would be linearly separable, only dot products are computed via a kernel function, most often radial basis or polynomial. An SVM is thus able to capture complex boundaries without ever working explicitly in the higher dimension. The method excels on smaller datasets with many features and is resistant to overfitting; its weaknesses are quadratic to cubic scaling with the number of samples and the need to normalise the data.<\/p>\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n<p class=\"wp-block-paragraph\">Imagine sheep and goats scattered across a meadow and you want to build a fence between them. There are infinitely many ways to route it. The naive solution builds the fence tight around the last animal \u2013 and all it takes is for one more sheep to be born a metre to the side next time and the fence is wrong. An SVM builds the fence so that it has the widest possible free strip on both sides. Interestingly, only the animals standing right at the edge of that strip have any say in where it goes; the herd in the middle of the meadow has no influence on the result at all.<\/p>\n","protected":false},"featured_media":0,"template":"","class_list":["post-9578","lexicon","type-lexicon","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.kubicek.ai\/en\/wp-json\/wp\/v2\/lexicon\/9578","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.kubicek.ai\/en\/wp-json\/wp\/v2\/lexicon"}],"about":[{"href":"https:\/\/www.kubicek.ai\/en\/wp-json\/wp\/v2\/types\/lexicon"}],"wp:attachment":[{"href":"https:\/\/www.kubicek.ai\/en\/wp-json\/wp\/v2\/media?parent=9578"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}