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What is SVM: Theory and Applications about?

Support vector machines (SVMs) are supervised learning models used for classification and regression analysis. The main objectives of SVMs are to find the hyperplane that maximizes the margin between classes and minimizes misclassification errors. SVMs work by mapping data points to a higher dimensional feature space so that data points can be separated with a clear gap that is as wide as possible. This allows for correct classification of both training and testing data points using a linear decision bounda

Author

Sreetam Ganguly

Language

EN