Hmm. Interesting indeed. And the real bonus is that i get it. Sounds like the solution to the problem of large numbers of target classes is to develop a sensible scheme for grouping the target classes s.t. you can reduce the number of them required per training run, then run the training once on each sub-set of target classes. To query, you simply query this large number of classifiers to build up your answer.
Is that the common practice? thank you for your time, Mr. Dunning. On Thu, Aug 1, 2013 at 5:37 PM, Ted Dunning <[email protected]> wrote: > I have talked to one user who had ~60,000 classes and they were able to use > OLR with success. > > The way that they did this was to arrange the output classes into a > multi-level tree. Then the trained classifiers at each level of the tree. > At any level, if there was a dominating result, then only that sub-tree > would be searched. Otherwise, all of the top few trees would be searched. > > Thus, execution would proceed by evaluating the classifier at the root of > the tree. One or more sub-trees would be selected. Each of the > classifiers at the roots of these sub-trees would be evaluated. This would > give a set of sub-sub-trees that eventually bottomed out with possible > answers. These possible answers are combined to get a final set of > categories. > > The detailed meanings of "dominating" and "top few" and "answers are > combined" are left as an exercise, but I think you can see the general > outline. The detailed definitions are very likely application specific in > any case. > > > > On Thu, Aug 1, 2013 at 11:25 AM, yikes aroni <[email protected]> wrote: > > > Say that I am trying to determine which customers buy particular candy > > bars. So I want to classify training data consisting of candy bar > > attributes (an N dimensional vector of variables) into customer > attributes > > (an M dimensional vector of customer attributes). > > > > Is there a preferred method when N and M are large? That is say 100 or > > more? > > > > I have done binary classification using AdaptiveLogisticRegression and > > OnlineLogisticRegression and small numbers of input features with > relative > > success. As I'm trying to implement this for large N and M, I feel like > i'm > > veering into the woods. Is there a code example anyone can point me to > that > > uses mahout libraries to do multi-class classification when the number of > > classes is large? > > >
