Maximum margin multi label structured prediction Conference Poster

Author(s): Lampert, Christoph
Title: Maximum margin multi label structured prediction
Affiliation IST Austria
Abstract: 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.
Conference Title: NIPS: Neural Information Processing Systems
ISBN: 10495258
Publisher: Neural Information Processing Systems  
Date Presented: 2011-12-13
Open access: no
IST Austria Authors
  1. Christoph Lampert
    87 Lampert
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