Maximum Margin Semi-Supervised Learning for Structured Variables
Abstract
Many real-world classification problems involve the prediction of multiple inter-dependent variables forming some structural depen- dency. Recent progress in machine learning has mainly focused on supervised classification of such structured variables. In this paper, we investigate structured classification in a semi-supervised setting. We present a discriminative approach that utilizes the intrinsic ge- ometry of input patterns revealed by unlabeled data points and we derive a maximum-margin formulation of semi-supervised learning for structured variables. Unlike transductive algorithms, our for- mulation naturally extends to new test points.
Cite
Text
Altun et al. "Maximum Margin Semi-Supervised Learning for Structured Variables." Neural Information Processing Systems, 2005.Markdown
[Altun et al. "Maximum Margin Semi-Supervised Learning for Structured Variables." Neural Information Processing Systems, 2005.](https://mlanthology.org/neurips/2005/altun2005neurips-maximum/)BibTeX
@inproceedings{altun2005neurips-maximum,
title = {{Maximum Margin Semi-Supervised Learning for Structured Variables}},
author = {Altun, Y. and McAllester, D. and Belkin, M.},
booktitle = {Neural Information Processing Systems},
year = {2005},
pages = {33-40},
url = {https://mlanthology.org/neurips/2005/altun2005neurips-maximum/}
}