Tighter Bounds for Structured Estimation

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.

• Type: Conference
• Year: 2008
• Where: NIPS
• Authors: Olivier Chapelle, Chuong B. Do, Quoc V. Le, Alexander J. Smola, Choon Hui Teo
• Keywords: Convex Upper Bound, Information Technology, Large-margin Structured Estimation, NIPS 2008, Optimization Algorithms