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Any thoughts on support vector machines & kernel methods? Is that stuff dead and buried, or what? (I've been out of the loop now for a while.)
by mathgenius 10y ago
Any thoughts on support vector machines & kernel methods? Is that stuff dead and buried, or what? (I've been out of the loop now for a while.)
- dr0l3 10y agoI don't have anything valid to back up my claims here, so take what i have with a grain of salt unless you find external validation for it. That being said i will venture a guess: Yes and no. Neural networks have a lot of nice properties like being easier to grasp, easier to parrallelize(?) and have better tools which i guess is because neural networks are applicable to more problems than SVM's because they are extensible.( see https://en.wikipedia.org/wiki/Recurrent_neural_network https://en.wikipedia.org/wiki/Recurrent_neural_network for example). Either way SVM's still do what they do very nicely and in the field of "Human Activity Recognition" where i did my thesis neural networks are practically never used but SVM's pop up from time to time.
- mathgenius 10y agoRight, scalability is a good point to consider. Along the same vein, my last ml job we ended up using gradient boosting which worked really well, but definitely does not scale to big data (afik.) Robustness is another thing to consider, both svm's and neural networks need a reasonable amount of data-massaging before they behave themselves. Hence the success in image processing where every pixel can be treated equally.
- wodenokoto 10y agoThere was a thread on reddit the other day about what problems a NN could solve that an SVM couldn't. Apparently there are things such as structured SVM's and SVM networks, and models where they attach an SVM instead of the output neurons. It isn't dead, and people are coming up with some pretty cool stuff, but NN seems to be where it is at right now.