Predicting structured objects with support vector machines

@article{Joachims2009PredictingSO,
  title={Predicting structured objects with support vector machines},
  author={Thorsten Joachims and Thomas Hofmann and Yisong Yue and Chun-Nam John Yu},
  journal={Commun. ACM},
  year={2009},
  volume={52},
  pages={97-104}
}
  • Thorsten Joachims, Thomas Hofmann, +1 author Chun-Nam John Yu
  • Published 2009
  • Computer Science
  • Commun. ACM
  • Machine Learning today offers a broad repertoire of methods for classification and regression. But what if we need to predict complex objects like trees, orderings, or alignments? Such problems arise naturally in natural language processing, search engines, and bioinformatics. The following explores a generalization of Support Vector Machines (SVMs) for such complex prediction problems. 

    Figures, Tables, and Topics from this paper.

    Citations

    Publications citing this paper.
    SHOWING 1-10 OF 96 CITATIONS

    Research Statement of Chun-nam Yu Structured Output Learning

    VIEW 6 EXCERPTS
    CITES BACKGROUND
    HIGHLY INFLUENCED

    Hybrid Decision Tree Architecture Utilizing Local SVMs for Efficient Multi-Label Learning

    VIEW 1 EXCERPT
    CITES METHODS

    Optimizing for Measure of Performance in Max-Margin Parsing

    Chinese base-NP recognition with HMSVM method

    • Qi Hui, Wang Zhong-hua
    • Computer Science
    • 2010 International Conference on Computer Application and System Modeling (ICCASM 2010)
    • 2010

    Large classifier systems in bio- and cheminformatics

    VIEW 1 EXCERPT
    CITES METHODS

    FILTER CITATIONS BY YEAR

    2009
    2020

    CITATION STATISTICS

    • 10 Highly Influenced Citations

    References

    Publications referenced by this paper.
    SHOWING 1-4 OF 4 REFERENCES

    Max-Margin Markov Networks

    VIEW 8 EXCERPTS
    HIGHLY INFLUENTIAL

    Essential Pages

    VIEW 6 EXCERPTS
    HIGHLY INFLUENTIAL

    hofmann, t. hierarchical document categorization with support vector machines

    • L. Cai
    • Proceedings of the ACM Conference on Information and Knowledge Management (CIKM)
    • 2004
    VIEW 3 EXCERPTS
    HIGHLY INFLUENTIAL