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Well, isn't it a bit awkward when dataset size is what forces you to select a certain algorithm that you otherwise wouldn't use? "Gee, I would love to train a n
by gcr 13y ago
Well, isn't it a bit awkward when dataset size is what forces you to select a certain algorithm that you otherwise wouldn't use? "Gee, I would love to train a neural network, but it's just so much data and I'm on a deadline; maybe I should just use an SVM and hope for the best ... ..."
That's one of the big surprises behind deep learning these days: it's now feasible to do things like "train a big neural network on bunches of images" in a sensible time. It's an optimization thing as much as it is a machine learning thing, in my opinion.
- tormeh 13y agoI didn't mean that training time is the constraint. It's that when training a more general algorithms (ANN) your hypothesis space has more dimensions than when training a more specialized one (SVM). You therefore need more data to train an ANN than a SVM. The reason for choosing SVM is not that your dataset is too big for ANN, it's that it's too small for ANN.