Tutorial on Support Vector Machines and using them in MATLAB

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A support vector machine (SVM) is a popular machine learning technique that delivers highly accurate, compact models. The learning algorithm optimizes decision boundaries to minimize classification errors and transformations of the feature space using kernel functions that help separate classes.

Learn how support vector machines work and how kernel transformations increase the separability of classes. Also learn how to train SVMs interactively in MATLAB® using the Classification Learner app, visually interpret the decision boundaries that separate the classes, and compare these results with other machine learning algorithms.

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wow that Video was beautiful and perfect - 3 minutes for 2 months of lessons!

goldlenz

How could I plot the decision boundaries for the model trained and exported from Classification Learner? You mentioned the SVM documentation, but those examples work with the fitcsvm function and not models exported from Classification learner. I used this app to export a SVM model and I am struggling with getting the contour to graph the decision boundaries, so that I would be grateful if you helped me.

marioandresrodriguez

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