NIPS Proceedingsβ

Empirical Risk Minimization with Approximations of Probabilistic Grammars

Part of: Advances in Neural Information Processing Systems 23 (NIPS 2010)

[PDF] [BibTeX] [Supplemental]



Probabilistic grammars are generative statistical models that are useful for compositional and sequential structures. We present a framework, reminiscent of structural risk minimization, for empirical risk minimization of the parameters of a fixed probabilistic grammar using the log-loss. We derive sample complexity bounds in this framework that apply both to the supervised setting and the unsupervised setting.