2010
NeurIPS
NeurIPS 2010
Empirical Risk Minimization with Approximations of Probabilistic Grammars
Abstract
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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Interdisciplinary Bridge
- Artificial Intelligence and Interdisciplinary and Mathematics & Optimization
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Trend Setter
- Computational Linguistics
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Hot Topic Early Bird
- generative model
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Optimization & Theory > Learning Theory
Machine Learning > Optimization & Theory > Statistical Learning
Deep Learning > Models > Generative Models
Interdisciplinary > Linguistics > Computational Linguistics
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Mathematics & Optimization > Probability
Machine Learning > Core Methods > Structured Prediction