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Magic Moments for Structured Output Prediction.
- Source :
-
Journal of Machine Learning Research . 12/1/2008, Vol. 9 Issue 12, p2803-2846. 44p. 8 Diagrams, 7 Charts, 6 Graphs. - Publication Year :
- 2008
-
Abstract
- Most approaches to structured output prediction rely on a hypothesis space of prediction functions that compute their output by maximizing a linear scoring function. In this paper we present two novel learning algorithms for this hypothesis class, and a statistical analysis of their performance. The methods rely on efficiently computing the first two moments of the scoring function over the output space, and using them to create convex objective functions for training. We report extensive experimental results for sequence alignment, named entity recognition, and RNA secondary structure prediction. [ABSTRACT FROM AUTHOR]
- Subjects :
- *PREDICTION theory
*MACHINE learning
*DISCRIMINANT analysis
*HYPOTHESIS
*ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 15324435
- Volume :
- 9
- Issue :
- 12
- Database :
- Academic Search Index
- Journal :
- Journal of Machine Learning Research
- Publication Type :
- Academic Journal
- Accession number :
- 47676559