Overview of Support Vector Machines (SVM) SVM is a supervised machine learning algorithm used for classification and regression tasks. It works by finding a hyperplane that best separates different ...

Objective: To implement Support Vector Machines for both linear and non-linear (RBF kernel) classification, visualize decision boundaries, and evaluate model performance using cross-validation and ...

Finally, after implementing SVM for multiclass classification problems, you need to evaluate the performance of the model using some metrics, such as accuracy, precision, recall, or F1-score.

Abstract: In order to overcome some shortages of SVM, an improved classification model is introduced in this paper. For the first problem about isolated points or noises mixed in training data sets ...