760. SVM in High-Dimensional Settings
hard
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?
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?