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....[Show more]
|Collections||ANU Research Publications|
|Source:||Advances in Neural Information Processing Systems 21|
|01_Chapelle_Tighter_bounds_for_structured_2008.pdf||28.6 kB||Adobe PDF||Request a copy|
|02_Chapelle_Tighter_bounds_for_structured_2008.pdf||233.94 kB||Adobe PDF||Request a copy|
|03_Chapelle_Tighter_bounds_for_structured_2008.pdf||16.71 kB||Adobe PDF||Request a copy|
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