Thanks for the pointer, David. I'll check it out. NB and SGD appear to get most of the attention, at least as examples, so those are the algorithms I've played with most.
David On Thu, Jun 07, 2012 at 05:39:16PM -0500, David Kincaid wrote: > David, I'm not an expert here, but I think you want to be looking at the > LDA algorithm especially if documents could potentially be in more than one > topic. More info here: > https://cwiki.apache.org/confluence/display/MAHOUT/Latent+Dirichlet+Allocation > > > > On Thu, Jun 7, 2012 at 5:31 PM, David Engel <[email protected]> wrote: > > > Hi, > > > > I've been dabbling with Mahout off and on for a few months preparing > > for a classification project. It's now time to stop experimenting and > > do something for real. I've picked up a lot of things from following > > this list, but would like some advice regarding a few things before > > proceeding. I'll start with a very brief description of the project > > and then follow up with some questions. > > > > We need to classify potentially millions of documents into about 100 > > or so categories. Most documents will probably only belong to 1 > > category, but some will belong to several. It's also possible for > > some documents to not belong to any of the chosen categories. > > > > As noted, we need to handle the case where a document belongs to > > multiple categories. My understanding is the classification > > algorithms are primarily geared to classifying an item into one > > category and we would need run multiple classifiers in parallel to > > match multiple categories. Is that correct? I found something in the > > subversion logs referencing "multilabel" support that sounded > > interesting, but it was removed a few weeks ago. Is that of any > > relevance? > > > > Also as noted, we need to handle the case where a document belongs to > > no categories. Do any of the classification algorithms support the > > concept of an implicit "other" or "none" category or do we need to add > > an explicit one? If the latter, how many training samples do we need > > to use compared to the number of samples for the target categories? > > > > Finally, I recall seeing on this list that some of the classification > > algorithms break down if more than 20 to 30 categories are used and > > that multiple classifiers should be used hierarchically when more > > categories are needed. Is that still correct? If so, is there any > > preferred way to organize the cascaded classifiers? I'm currently > > analyzing the documents we will use for training to see which > > categories often, seldom or never occur together. > > > > David > > -- > > David Engel > > [email protected] > > -- David Engel [email protected]
