@inproceedings{bbdcf139ec14495bb2642e5667fd2783,
title = "Tighter bounds for structured estimation",
abstract = "Large-margin structured estimation methods minimize a convex upper bound of loss functions. While they allow for efficient optimization algorithms, these convex formulations are not tight and sacrifice the ability to accurately model the true loss. We present tighter non-convex bounds based on generalizing the notion of a ramp loss from binary classification to structured estimation. We show that a small modification of existing optimization algorithms suffices to solve this modified problem. On structured prediction tasks such as protein sequence alignment and web page ranking, our algorithm leads to improved accuracy.",
author = "Do, {Chuong B.} and Quoc Le and Teo, {Choon Hui} and Olivier Chapelle and Alex Smola",
year = "2009",
language = "English",
isbn = "9781605609492",
series = "Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference",
publisher = "Neural Information Processing Systems",
pages = "281--288",
booktitle = "Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference",
note = "22nd Annual Conference on Neural Information Processing Systems, NIPS 2008 ; Conference date: 08-12-2008 Through 11-12-2008",
}