SVM (margin intuition, kernel trick)

Margins and the kernel trick.

0/8

completed

hardSVM in High-Dimensional Settings

7/8

SVMs are known to work well in high-dimensional spaces. What property of the SVM makes it suitable when the number of features exceeds the number of samples?