---
_id: '3322'
abstract:
- lang: eng
text: We study multi-label prediction for structured output spaces, a problem that
occurs, for example, in object detection in images, secondary structure prediction
in computational biology, and graph matching with symmetries. Conventional multi-label
classification techniques are typically not applicable in this situation, because
they require explicit enumeration of the label space, which is infeasible in case
of structured outputs. Relying on techniques originally designed for single- label
structured prediction, in particular structured support vector machines, results
in reduced prediction accuracy, or leads to infeasible optimization problems.
In this work we derive a maximum-margin training formulation for multi-label structured
prediction that remains computationally tractable while achieving high prediction
accuracy. It also shares most beneficial properties with single-label maximum-margin
approaches, in particular a formulation as a convex optimization problem, efficient
working set training, and PAC-Bayesian generalization bounds.
article_processing_charge: No
author:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: Lampert C. Maximum Margin Multi Label Structured Prediction. Neural
Information Processing Systems Foundation; 2011.
apa: 'Lampert, C. (2011). Maximum margin multi label structured prediction.
NIPS: Neural Information Processing Systems. Neural Information Processing
Systems Foundation.'
chicago: 'Lampert, Christoph. Maximum Margin Multi Label Structured Prediction.
NIPS: Neural Information Processing Systems. Neural Information Processing
Systems Foundation, 2011.'
ieee: C. Lampert, Maximum margin multi label structured prediction. Neural
Information Processing Systems Foundation, 2011.
ista: Lampert C. 2011. Maximum margin multi label structured prediction, Neural
Information Processing Systems Foundation,p.
mla: 'Lampert, Christoph. “Maximum Margin Multi Label Structured Prediction.” NIPS:
Neural Information Processing Systems, Neural Information Processing Systems
Foundation, 2011.'
short: C. Lampert, Maximum Margin Multi Label Structured Prediction, Neural Information
Processing Systems Foundation, 2011.
date_created: 2018-12-11T12:02:40Z
date_published: 2011-12-13T00:00:00Z
date_updated: 2023-10-17T11:47:36Z
day: '13'
department:
- _id: ChLa
language:
- iso: eng
month: '12'
oa_version: None
publication: 'NIPS: Neural Information Processing Systems'
publication_status: published
publisher: Neural Information Processing Systems Foundation
publist_id: '3313'
related_material:
record:
- id: '3163'
relation: earlier_version
status: public
status: public
title: Maximum margin multi label structured prediction
type: conference_poster
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2011'
...