January 2019
Intermediate to advanced
294 pages
6h 43m
English
Content preview from Machine Learning Quick Reference
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Kernel trick
We have already seen that SVM works smoothly when it comes to having linear separable data. Just have a look at the following figure; it depicts that vectors are not linearly separable, but the noticeable part is that it is not being separable in 2D space:
With a few adjustments, we can still make use of SVM here.
Transformation of a two-dimensional vector into a 3D vector or any other higher dimensional vector can set things right for us. The next step would be to train the SVM using a higher dimensional vector. But the question arises of how high in dimension we should go to transform the vector. What this means is if the transformation ...
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