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MOSFETBJT t1_is6brc5 wrote

In data science, subjects like this go through ebbs and flows of usage and popularity. In early 2000s SVMs were all the rage.

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BrisklyBrusque t1_is7g6gk wrote

SVMs prevailed against neural networks in a big image classification contest in 2006. Then they fell out of favor, with other learning algorithms like

•Adaboost

•C4.5

•Decision stumps

•Multivariate adaptive regression splines

•Flexible discriminant analysis

•Arcing

•Wagging

Not sure which of these will come back, but it’s funny how often ideas are rediscovered (like neural networks themselves, which were branded as multilayer perceptrons initially)

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