Sure. If you want 1 of n categories, then train a single n-way classifier.
If you want k of n, train n binary classifiers or hack the 1 of n classifier slightly to remove the soft-max function. If there are a relatively small number of category combinations, train on each combination as a 1 of n target. There are some fancier algorithms that make use of the categorical combinations, but we don't need to worry about those to start. On Thu, Jan 21, 2010 at 2:55 PM, Jason Rutherglen < [email protected]> wrote: > > The SGD and Pegasos classifiers would be ideal for this. > > For multiple categories? From a user perspective, classifying > into multiple categories would be real sweet because it'd save > time and be better than how CL behaves today (eg, uni-category). > Also Solr/Lucene easily supp -- Ted Dunning, CTO DeepDyve
