Dietterich 2008 gradient tree boosting for training conditional random fields
Contents
Citation
Gradient Tree Boosting for Training Conditional Random Fields. By T. G Dietterich, G. Hao, A. Ashenfelter. In Journal of Machine Learning Research, vol. 9 ({{{issue}}}), 2008.
Online Version
This Paper is available online [1].
Summary
The paper addresses the problem of combinatorial explosion of parameters of CRFs when new features are introduced. It represents the potential functions as sums of regression trees. The authors claim that adding a regression tree is a big step in the feature space and hence it reduces the number of iterations. This leads to a significant performance improvement.
Method
Functional Gradient Descent
In a traditional gradient descent method, a function is represented by linear function of parameters. Thus, the parameter values after steps would be . Instead of parameterizing, functional gradient ascent assumes that is a weighted sum of functions; .