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Support vector machines are not particularly interesting. They are just perceptrons with a lot of math. So ultimately souped-up linear classifiers. If you start
by TeodolfoM 12y ago
Support vector machines are not particularly interesting. They are just perceptrons with a lot of math. So ultimately souped-up linear classifiers. If you start learning the kernel in an SVM you get a deeper model. A neural net using the hinge loss and an L2 penalty on the weights is basically a primal SVM.
- abhgh 12y agoThe math in "a lot of math" and the soup in "souped-up linear classifiers" are really not stuff to be casually swept under the carpet :) They still remain one of the best off-the-shelf learners one can use (look at "An Empirical Comparison of Supervised Learning Algorithms" by Rich Caruana et al).