Hi All, I am trying to use maxent for the Large scale hierarchical challenge ( http://lshtc.iit.demokritos.gr:10000/ ) contest.
However, I could not get maxent to work on large number of classes/categories ( dmoz test data has something like 28K classes and 580K+ features ). So decided to split the training and merging the models after every few iterations. The split is decided by the category/classes so that all the instance belonging to one class resides in one split. At every few iteration the model generated by each of these splits is merged ( I merge out all of the model Data structures ) and average out the parameters estimated. But even after something like 1000 iterations I don't see accuracy going beyond 70%. As after every merge there is dip in overall accuracy. So I was wondering if there is a better way to merge. Can someone guide me in getting the split / incremental training or should I try the perceptron model . --Thanks and Regards Vaijanath N. Rao
