I have a lots of data from where I work. The data are documents (title + abstract) and each document can have one or more categories (e.g. social sciences + policics). We want to build a recommender and analyze the output for further testing.
Thanks and regards, David 2011/11/8 Ted Dunning <[email protected]> > The practical techniques for such problems are pretty diverse. > > One method is to simply define multiple binary classifiers. If you can > stratify your labels, then you can have some labels depend on others. > Another option is to find commonly occurring sets of labels and build > classifiers for those sets directly. > > Do you have an application mind? Do you have data available? > > On Tue, Nov 8, 2011 at 6:37 AM, David Rahman <[email protected] > >wrote: > > > Yes, I was asking for an example where multiple labels might be aplied > to a > > single example. > > > > Thanks and regards, > > David > > > > 2011/11/8 Ted Dunning <[email protected]> > > > > > What exactly do you mean by multi-label classification? > > > > > > The 20 newsgroup example has many possible label values. > > > > > > Are you asking for an example where multiple labels might be applied > to a > > > single example? If so, no, we don't have a nice example of that. > > > > > > On Tue, Nov 8, 2011 at 5:36 AM, David Rahman < > [email protected] > > > >wrote: > > > > > > > Hi, > > > > > > > > I have a general question about multi-label classification. Binary- > or > > > > single-label classification is working, as shown in several examples > > > > (Wikipedia and 20Newsgroup, Mahout In Action book...). > > > > > > > > Are there some working examples on multi-label calssification for > > trying > > > > out? > > > > Or is there some data available on how mahout performs on mult-label > > > > classification problems? > > > > > > > > Thanks and regards, > > > > David > > > > > > > > > >
