Question: Support vector machines can be extended to work with nonlinear classification boundaries by using the kernel trick projecting the feature space onto a lower dimensional

Support vector machines can be extended to work with nonlinear classification boundaries by
using the kernel trick
projecting the feature space onto a lower dimensional space
incorporating polynomial regression
modifying the standard sigmoid function
Support vector machines can be extended to work with nonlinear classification

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