> Prediction is not that slow with linear SVMs especially not compared to something like K-NN.
Provided your structural dimensionality is below about 10 (ie. 10 dominant eigenvalues for your features), then KNN can be O(log(N)) for prediction via a well designed Kd-Tree.
KNN is also really simple to understand, and to design features for. It also never really tends to throw up surprises, which for production is the kind of thing you want. Most importantly, the failures tend to 'make sense' to humans, so you stay out of the uncanny valley.
Provided your structural dimensionality is below about 10 (ie. 10 dominant eigenvalues for your features), then KNN can be O(log(N)) for prediction via a well designed Kd-Tree.
KNN is also really simple to understand, and to design features for. It also never really tends to throw up surprises, which for production is the kind of thing you want. Most importantly, the failures tend to 'make sense' to humans, so you stay out of the uncanny valley.