Large Margin vs. Large Volume in Transductive Learning
Abstract
We consider a large volume principle for transductive learning that prioritizes the transductive equivalence classes according to the volume they occupy in hypothesis space. We approximate volume maximization using a geometric interpretation of the hypothesis space. The resulting algorithm is defined via a non-convex optimization problem that can still be solved exactly and efficiently. We provide a bound on the test error of the algorithm and compare it to transductive SVM (TSVM) using 31 datasets.
Cite
Text
El-Yaniv et al. "Large Margin vs. Large Volume in Transductive Learning." Machine Learning, 2008. doi:10.1007/S10994-008-5071-9Markdown
[El-Yaniv et al. "Large Margin vs. Large Volume in Transductive Learning." Machine Learning, 2008.](https://mlanthology.org/mlj/2008/elyaniv2008mlj-large/) doi:10.1007/S10994-008-5071-9BibTeX
@article{elyaniv2008mlj-large,
title = {{Large Margin vs. Large Volume in Transductive Learning}},
author = {El-Yaniv, Ran and Pechyony, Dmitry and Vapnik, Vladimir},
journal = {Machine Learning},
year = {2008},
pages = {173-188},
doi = {10.1007/S10994-008-5071-9},
volume = {72},
url = {https://mlanthology.org/mlj/2008/elyaniv2008mlj-large/}
}