Trajectory Regression on Road Networks
Tsuyoshi Idé, Masashi Sugiyama
AAAI 2011
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.
Tsuyoshi Idé, Masashi Sugiyama
AAAI 2011
Gosia Lazuka, Andreea Simona Anghel, et al.
SC 2024
Rutu Mulkar-Mehta, Christopher Welty, et al.
AAAI 2011
Ben Fei, Jinbai Liu
IEEE Transactions on Neural Networks