Mistake-driven mixture of hierarchical tag context trees
Masahiko Haruno, Yūji Matsumoto · 1997
This paper proposes a mistake-driven mixture method for learning a tag model. The method iteratively performs two procedures: 1. constructing a tag model based on the current data distribution and 2. updating the distribution by focusing on data that are not well predicted by the constructed model. The final tag model is constructed by mixing all the models according to their performance. To well reflect the data distribution, we represent each tag model as a hierarchical tag (i.e., NTT <proper noun