No, they require a full pass over the support vectors, which are potentially a much smaller set. (That’s part of why everyone was so excited about SVMs when they were invented) The support vectors are the training values with nonzero hinge loss, or alternatively, training values sufficiently close to the decision boundary.
Fair enough, but the number of support vectors for non trivial problems is still pretty large (as I understand but could be wrong), e.g. 20-30% of the dataset. Having to iterate over 30% of say imagenet on each batch of predictions seems unfeasible.
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No, they require a full pass over the support vectors, which are potentially a much smaller set. (That’s part of why everyone was so excited about SVMs when they were invented) The support vectors are the training values with nonzero hinge loss, or alternatively, training values sufficiently close to the decision boundary.
Fair enough, but the number of support vectors for non trivial problems is still pretty large (as I understand but could be wrong), e.g. 20-30% of the dataset. Having to iterate over 30% of say imagenet on each batch of predictions seems unfeasible.