It makes no sense to compare the usefulness of SVMs to the usefulness of boosting
If an SVM outperforms a boosted-whatever, then it does make sense to compare them.
boosting operates on SVMs
Except that in practice no one uses SVMs as the base learners for boosting (as far as I know). I don’t think it would work very well, since basic SVMs are linear models, and adding multiple linear models is useless. Boosting is usually done with decision trees or decision stumps.
bag-of-words
That is a feature representation, and it has little to do with the learning method. You could encode a text as bag-of-words, and train an SVM on these features.
Reproducing Kernel Hilbert Space methods
Kernel SVM ″is″ a RKHS method, in fact, it is basically the prototypical one.
bag-of-words That is a feature representation, and it has little to do with the learning method. You could encode a text as bag-of-words, and train an SVM on these features.
Yes, sure, but the most generic way is just to look at a historgram distance between word occurrences. I guess that would generically fall under k-means or similar methods, but that’s what I was referring to by citing bag-of-words as a method on its own. Of course you can mix and match and cascade all of these to produce different methods.
Yes, sure, but the most generic way is just to look at a historgram distance between word occurrences. I guess that would generically fall under k-means or similar methods, but that’s what I was referring to by citing bag-of-words as a method on its own. Of course you can mix and match and cascade all of these to produce different methods.