Structured output prediction with support vector machines

Authors:
Thorsten Joachims
Affiliations:
Cornell University, Ithaca, NY
Venue:
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2006

Citing 15
Cited 1

Support-Vector Networks

Machine Learning
Foundations of statistical natural language processing

Foundations of statistical natural language processing
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data

ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Discriminative Reranking for Natural Language Parsing

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
On the algorithmic implementation of multiclass kernel-based vector machines

The Journal of Machine Learning Research
Support vector machine learning for interdependent and structured output spaces

ICML '04 Proceedings of the twenty-first international conference on Machine learning
Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Improving machine learning approaches to coreference resolution

ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
New ranking algorithms for parsing and tagging: kernels over discrete structures, and the voted perceptron

ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Learning structured prediction models: a large margin approach

Learning structured prediction models: a large margin approach
Large Margin Methods for Structured and Interdependent Output Variables

The Journal of Machine Learning Research
Supervised clustering with support vector machines

ICML '05 Proceedings of the 22nd international conference on Machine learning
A support vector method for multivariate performance measures

ICML '05 Proceedings of the 22nd international conference on Machine learning
Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms

EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
A discriminative matching approach to word alignment

HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing

Learning to predict from textual data

Journal of Artificial Intelligence Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

This abstract accompanying a presentation at S+SSPR 2006 explores the use of Support Vector Machines (SVMs) for predicting structured objects like trees, equivalence relations, or alignments. It is shown that SVMs can be extended to these problems in a well-founded way, still leading to a convex quadratic training problem and maintaining the ability to use kernels. While the training problem has exponential size, there is a simple algorithm that allows training in polynomial time. The algorithm is implemented in the SVM-Struct software, and it is discussed how the approach can be applied to problems ranging from natural language parsing to supervised clustering.