Search-based structured prediction
@article{Daum2009SearchbasedSP, title={Search-based structured prediction}, author={Hal Daum{\'e} and J. Langford and D. Marcu}, journal={Machine Learning}, year={2009}, volume={75}, pages={297-325} }
We present Searn, an algorithm for integrating search and learning to solve complex structured prediction problems such as those that occur in natural language, speech, computational biology, and vision. Searn is a meta-algorithm that transforms these complex problems into simple classification problems to which any binary classifier may be applied. Unlike current algorithms for structured learning that require decomposition of both the loss function and the feature functions over the predicted… CONTINUE READING
Figures, Tables, and Topics from this paper
Paper Mentions
518 Citations
Randomized Greedy Search for Structured Prediction: Amortized Inference and Learning
- Computer Science
- IJCAI
- 2019
- 1
- Highly Influenced
- PDF
A Neural Probabilistic Structured-Prediction Method for Transition-Based Natural Language Processing
- Computer Science
- J. Artif. Intell. Res.
- 2017
- 6
- PDF
ℋC-search for structured prediction in computer vision
- Computer Science
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- 2015
- 29
- PDF
References
SHOWING 1-10 OF 101 REFERENCES
Learning as search optimization: approximate large margin methods for structured prediction
- Mathematics, Computer Science
- ICML '05
- 2005
- 273
- PDF
Learning structured prediction models: a large margin approach
- Mathematics, Computer Science
- ICML '05
- 2005
- 528
- PDF
Large Margin Methods for Structured and Interdependent Output Variables
- Computer Science
- J. Mach. Learn. Res.
- 2005
- 2,186
- PDF
Gaussian process classification for segmenting and annotating sequences
- Mathematics, Computer Science
- ICML '04
- 2004
- 78
- PDF
Bidirectional Inference with the Easiest-First Strategy for Tagging Sequence Data
- Computer Science
- HLT/EMNLP
- 2005
- 272
- PDF
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
- Computer Science
- J. Mach. Learn. Res.
- 2005
- 1,282
- PDF