Magic Moments for Structured Output Prediction
Abstract
Most approaches to structured output prediction rely on a hypothesis space of prediction functions that compute their output by maximizing a linear scoring function. In this paper we present two novel learning algorithms for this hypothesis class, and a statistical analysis of their performance. The methods rely on efficiently computing the first two moments of the scoring function over the output space, and using them to create convex objective functions for training. We report extensive experimental results for sequence alignment, named entity recognition, and RNA secondary structure prediction.
Cite
Text
Ricci et al. "Magic Moments for Structured Output Prediction." Journal of Machine Learning Research, 2008.Markdown
[Ricci et al. "Magic Moments for Structured Output Prediction." Journal of Machine Learning Research, 2008.](https://mlanthology.org/jmlr/2008/ricci2008jmlr-magic/)BibTeX
@article{ricci2008jmlr-magic,
title = {{Magic Moments for Structured Output Prediction}},
author = {Ricci, Elisa and De Bie, Tijl and Cristianini, Nello},
journal = {Journal of Machine Learning Research},
year = {2008},
pages = {2803-2846},
volume = {9},
url = {https://mlanthology.org/jmlr/2008/ricci2008jmlr-magic/}
}