{"id":21217,"date":"2021-07-07T06:01:55","date_gmt":"2021-07-07T05:01:55","guid":{"rendered":"https:\/\/www.inovex.de\/blog\/?p=21217"},"modified":"2024-01-18T10:29:57","modified_gmt":"2024-01-18T09:29:57","slug":"support-vector-machines-guide","status":"publish","type":"post","link":"https:\/\/www.inovex.de\/de\/blog\/support-vector-machines-guide\/","title":{"rendered":"Extensive Guide to Support Vector Machines"},"content":{"rendered":"
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In this post we will talk about a fundamental model in machine learning \u2013 support vector machines (short: SVMs). We will take a detailed look at the theory behind SVMs and implement and train an SVM in plain Python. In particular, we will consider the two approaches for formulating SVMs: the primal and the dual approach. In the end, we will further consider the usage of kernel functions to apply SMVs to non-linear problems.<\/p>\n<\/div>\n<\/div>\n<\/div>\n

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