--- _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' ...