Question: SVM with kernel tricks could map the non - linear separable data set into a higher dimensional space where we can find a hyperplane to

SVM with kernel tricks could map the non-linear separable data set into a higher dimensional space where we can find a hyperplane to separate the samples linearly. TrueO False

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