Introduction to Support Vector Machines in Machine Learning

Re:Introduction to Support Vector Machines in Machine Learning by starchild_3001 on Thursday April 30, 2020 @09:18PM (#23038178)

I've been an ML practioner since 2009. I've used every method imaginable or popular, I think. With the exception of non-linear SVMs. Linear SVM => All good, just the hingle loss optimization. Non-linear SVM, a bit of overkill with basis expansion. Just too slow, or too complex a model?

My impression: SVMs are more of theoretical interest than practical interest. Yeah, learn your statistics. Loss functions. Additive models. Neural nets. Linear models. Decision trees, kNNs etc. SVM is more of a special interest, imho.

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Re:Introduction to Support Vector Machines in Machine Learning by rangerranvir on Friday May 01, 2020 @12:45AM (#23039219)

We can definitely learn a piece from such an experienced practitioner. Thanks for sharing, I think your intuition matches with the other experienced once in the comments.

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