@article{Tsuboi_Unno_Kashima_Okazaki_2011, title={Fast Newton-CG Method for Batch Learning of Conditional Random Fields}, volume={25}, url={https://ojs.aaai.org/index.php/AAAI/article/view/7894}, DOI={10.1609/aaai.v25i1.7894}, abstractNote={ <p> We propose a fast batch learning method for linear-chain Conditional Random Fields (CRFs) based on Newton-CG methods. Newton-CG methods are a variant of Newton method for high-dimensional problems. They only require the Hessian-vector products instead of the full Hessian matrices. To speed up Newton-CG methods for the CRF learning, we derive a novel dynamic programming procedure for the Hessian-vector products of the CRF objective function. The proposed procedure can reuse the byproducts of the time-consuming gradient computation for the Hessian-vector products to drastically reduce the total computation time of the Newton-CG methods. In experiments with tasks in natural language processing, the proposed method outperforms a conventional quasi-Newton method. Remarkably, the proposed method is competitive with online learning algorithms that are fast but unstable. </p> }, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Tsuboi, Yuta and Unno, Yuya and Kashima, Hisashi and Okazaki, Naoaki}, year={2011}, month={Aug.}, pages={489-494} }