Date of Original Version

12-2010

Type

Conference Proceeding

Journal Title

Advances in Neural Information Processing Systems

Volume

23

Abstract or Description

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.

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Published In

Advances in Neural Information Processing Systems, 23.