{"id":13469,"date":"2020-03-25T17:59:52","date_gmt":"2020-03-25T12:29:52","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/introduction-to-support-vector-machine\/"},"modified":"2025-03-18T12:45:27","modified_gmt":"2025-03-18T07:15:27","slug":"introduction-to-support-vector-machine","status":"publish","type":"post","link":"https:\/\/www.mygreatlearning.com\/blog\/introduction-to-support-vector-machine\/","title":{"rendered":"Support Vector Machine (SVM) Algorithm"},"content":{"rendered":"\n

Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification and regression tasks. It is widely applied in fields like image recognition<\/a>, text classification, and bioinformatics due to its efficiency in handling high-dimensional data.<\/p>\n\n\n\n

In this article, we will start from the basics of SVM in machine learning<\/strong>, gradually diving into its working principles, different types, mathematical formulation, real-world applications, and implementation.<\/p>\n\n\n\n

What is Support Vector Machine (SVM)?<\/strong><\/h2>\n\n\n\n

SVM is a classification algorithm that finds the best boundary (hyperplane) to separate different classes in a dataset. It works by identifying key data points, called support vectors<\/strong>, that influence the position of this boundary, ensuring maximum separation between categories.<\/p>\n\n\n\n

For example, if we have a dataset of emails labeled as spam and not spam, SVM will create a decision boundary that best separates these two groups based on features like word frequency and message length.<\/p>\n\n\n\n