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Nut-Nut Nut
Nut-Nut Nut

Nut-Nut Nut

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From Support Vector Machines Tutorial by Abhishek Ghose

How do SVMs deal with this? They allow you to specify how many errors you are willing to accept.

From Support Vector Machines Tutorial by Abhishek Ghose

…he middle of the two clusters, it is less “risky,” gives the data distributions for each class some wiggle room so to speak, and thus generalizes well on test data.

From Support Vector Machines Tutorial by Abhishek Ghose

…luster. Sure, it separates the training data perfectly, but if it sees a test point that’s a little farther out from the clusters, there is a good chance it would get the label wrong.