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If you know what the structure is, then you could write a proper kernel and then SVMs are better again. NNs win when you don't know what the structure is and it
by xapata 9y ago
If you know what the structure is, then you could write a proper kernel and then SVMs are better again. NNs win when you don't know what the structure is and it's too complex to approximate with a standard kernel.
- Houshalter 9y agoI'm not aware of anything like a convolutional SVM, but I'm not terribly familiar with them. As I understand it SVMs are fundamentally shallow learners, and if you try to hack them to make them deep and recurrent or convolutional, you just get weird NNs.
- xapata 9y agoShallow in the sense that SVMs are not layered, but the layering of a NN is only to enable the humble perceptron (logistic or sigmoid, usually) to model more complex functions. In contrast, the SVM doesn't need to layer, because the kernel can be arbitrarily complex.