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Support-Vector-Machine

Support Vector Machine for educational purposes.

To understand how maximum margin formulation works one can examine this code.

You can pick the kernel you wuld like to work on. The examples below represents Gaussian and Polynomial Kernel. You can also change the data set as you wish. You may vary the standard deviation and the mean from classA and ClassB.

By changing the Kernel parameters you can observe the changes in decision boundaries. You can observe the overfitting problem and the effects of the Kernel parameters to overfitting.

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Support Vector Machine

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