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A semiparametric generative model for efficient structured-output supervised learning
2008
Annals of Mathematics and Artificial Intelligence
We present a semiparametric generative model for supervised learning with structured outputs. The main algorithmic idea is to replace the parameters of an underlying generative model (such as a stochastic grammars) with input-dependent predictions obtained by (kernel) logistic regression. This method avoids the computational burden associated with the comparison between target and predicted structure during the training phase, but requires as an additional input a vector of sufficient
doi:10.1007/s10472-009-9137-6
fatcat:twnw6o5osrcozcl6hwtovvwwbu