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Understanding Support Vector Machines by Arunprakash is a document available to read on EtoBox.

Support Vector Machine (SVM) is a supervised learning algorithm used for classification and regression by finding the optimal separating hyperplane that maximizes the margin between classes. It includes linear and non-linear types, utilizing kernel functions such as linear, polynomial, and RBF to handle various data separability. While SVM is effective for high-dimensional data and robust to overfitting, it can be computationally expensive and requires careful selection of kernels and parameters.

Author

Arunprakash

Language

EN