Agreement-Based Learning
Abstract
The learning of probabilistic models with many hidden variables and nondecomposable dependencies is an important and challenging problem. In contrast to traditional approaches based on approximate inference in a single intractable model, our approach is to train a set of tractable submodels by encouraging them to agree on the hidden variables. This allows us to capture non-decomposable aspects of the data while still maintaining tractability. We propose an objective function for our approach, derive EM-style algorithms for parameter estimation and demonstrate their effectiveness on three challenging real-world learning tasks.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 08, 2008
- Accession Number
- ADA628521
Entities
People
- Dan Klein
- Michael I. Jordan
- Percy Liang
Organizations
- University of California, Berkeley