Support Vector Machine for Regression
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Support Vector Machine (SVM) is a widely-used machine learning algorithm for regression problems, fully implemented within MATLAB's computational environment. The SVM regression implementation utilizes key functions such as fitrsvm for model training and predict for generating predictions, employing epsilon-insensitive loss functions to handle complex non-linear relationships. This approach effectively solves challenging data modeling and prediction tasks through kernel trick implementations (linear, polynomial, RBF) that map data to higher-dimensional spaces. The MATLAB implementation provides convenient parameter tuning options for kernel selection, box constraints, and epsilon values, making regression operations straightforward and efficient for handling various regression complexities with minimal coding effort.
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