54 search results - page 1 / 11 » On the efficient minimization of convex surrogates in superv... |
NIPS
2008
13 years 6 months ago
2008
Bartlett et al (2006) recently proved that a ground condition for convex surrogates, classification calibration, ties up the minimization of the surrogates and classification risk...
ICPR
2008
IEEE
13 years 11 months ago
2008
IEEE
ML
2007
ACM
13 years 4 months ago
2007
ACM
Abstract Surrogate maximization (or minimization) (SM) algorithms are a family of algorithms that can be regarded as a generalization of expectation-maximization (EM) algorithms. A...
JMLR
2012
11 years 7 months ago
2012
We consider the task of devising large-margin based surrogate losses for the learning to rank problem. In this learning to rank setting, the traditional hinge loss for structured ...
ACL
2008
13 years 6 months ago
2008
We present a novel semi-supervised training algorithm for learning dependency parsers. By combining a supervised large margin loss with an unsupervised least squares loss, a discr...