Question: The basic idea as to why SVMs can find decision boundaries for data that is not linearly separable is that... a ) they can project

The basic idea as to why SVMs can find decision boundaries for data that is not linearly separable is that...
a)they can project the data into a space where it is linearly separable
b)they maximize the decision margin
c)they allow to separate data into more than 2 classes
d)they are universal approximators

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