TY - GEN
T1 - Spectral regularization for max-margin sequence tagging
AU - Quattoni, Ariadna
AU - Balle, Borja
AU - Carreras, Xavier
AU - Globerson, Amir
N1 - Publisher Copyright:
Copyright 2014 by the author(s).
PY - 2014
Y1 - 2014
N2 - We frame max-margin learning of latent variable structured prediction models as a convex optimization problem, making use of scoring functions computed by input-output observable operator models. This learning problem can be expressed as an optimization problem involving a low-rank Hankel matrix that represents the input-output operator model. The direct outcome of our work is a new spectral regularization method for max-margin structured prediction. Our experiments confirm that our proposed regularization framework leads to an effective way of controlling the capacity of structured prediction models.
AB - We frame max-margin learning of latent variable structured prediction models as a convex optimization problem, making use of scoring functions computed by input-output observable operator models. This learning problem can be expressed as an optimization problem involving a low-rank Hankel matrix that represents the input-output operator model. The direct outcome of our work is a new spectral regularization method for max-margin structured prediction. Our experiments confirm that our proposed regularization framework leads to an effective way of controlling the capacity of structured prediction models.
UR - http://www.scopus.com/inward/record.url?scp=84919831461&partnerID=8YFLogxK
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AN - SCOPUS:84919831461
T3 - 31st International Conference on Machine Learning, ICML 2014
SP - 3698
EP - 3706
BT - 31st International Conference on Machine Learning, ICML 2014
PB - International Machine Learning Society (IMLS)
T2 - 31st International Conference on Machine Learning, ICML 2014
Y2 - 21 June 2014 through 26 June 2014
ER -