Learning and Inference over Constrained Output
Abstract
We study learning structured output in a discriminative framework where values of the output variables are estimated by local classifiers. In this framework, complex dependencies among the output variables are captured by constraints and dictate which global labels can be inferred. We compare two strategies, learning independent classifiers and inference based training, by observing their behaviors in different conditions. Experiments and theoretical justification lead to the conclusion that using inference based learning is superior when the local classifiers are difficult to learn but may require many examples before any discernible difference can be observed. 1
Cite
Text
Punyakanok et al. "Learning and Inference over Constrained Output." International Joint Conference on Artificial Intelligence, 2005.Markdown
[Punyakanok et al. "Learning and Inference over Constrained Output." International Joint Conference on Artificial Intelligence, 2005.](https://mlanthology.org/ijcai/2005/punyakanok2005ijcai-learning/)BibTeX
@inproceedings{punyakanok2005ijcai-learning,
title = {{Learning and Inference over Constrained Output}},
author = {Punyakanok, Vasin and Roth, Dan and Yih, Wen-tau and Zimak, Dav},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2005},
pages = {1124-1129},
url = {https://mlanthology.org/ijcai/2005/punyakanok2005ijcai-learning/}
}