#StackBounty: #svm #kernel-trick #supervised-learning #rbf-kernel #radial-basis Relationship between structural or statistical properti…

Bounty: 50

I am trying to understand the relationship between structural or statistical properties of training dataset and hardness of classification in the context of binary classification with SVM using RBF kernel. I would like to predict the hardness without actually trying to classify the dataset. The obvious properties are the size of the dataset and features. What other meta-properties indicate that an SVM using RBF kernel will result in low accuracy and/or extremely expensive computation?

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