Support Vector Regression Implementation in MATLAB
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Support Vector Regression (SVR) implementation in MATLAB enables numerous advanced functionalities. Beyond standard regression and classification tasks, SVR can be utilized for probability estimation, outlier handling, robust regression, and nonlinear regression applications. The kernel functions employed in SVR significantly enhance the handling of nonlinear problems through appropriate kernel selection and parameter optimization. For MATLAB implementation, key functions include fitrsvm for regression model training and predict for making predictions, with kernel options such as linear, polynomial, and radial basis function (RBF). Parameter tuning through cross-validation using fitrsvm's 'OptimizeHyperparameters' option ensures optimal model performance. Therefore, SVR serves as an essential tool for regression tasks, particularly when dealing with complex, high-dimensional datasets requiring robust and nonlinear modeling approaches.
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