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04_svm_kernels.ipynb Ch 4 Update: kernel fix and add eval code Apr 9, 2018
04_svm_kernels.py Ch 4 Update: kernel fix and add eval code Apr 9, 2018
readme.md Links with relative paths Feb 5, 2017

readme.md

Working with Kernels

Linear SVMs are very powerful. But sometimes the data are not very linear. To this end, we can use the 'kernel trick' to map our data into a higher dimensional space, where it may be linearly separable. Doing this allows us to separate out non-linear classes. See the below example.

If we attempt to separate the below circular-ring shaped classes with a standard linear SVM, we fail.

Linear SVM Nonlinear Data

But if we separate it with a Gaussian-RBF kernel, we can find a linear separator in a higher dimension that works a lot better.

Gaussian Kernel Nonlinear Data

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