Support vector machine learning for interdependent and structured output spaces

@inproceedings{Tsochantaridis2004SupportVM,
  title={Support vector machine learning for interdependent and structured output spaces},
  author={Ioannis Tsochantaridis and Thomas Hofmann and Thorsten Joachims and Yasemin Altun},
  booktitle={ICML},
  year={2004}
}
Learning general functional dependencies is one of the main goals in machine learning. Recent progress in kernel-based methods has focused on designing flexible and powerful input representations. This paper addresses the complementary issue of problems involving complex outputs such as multiple dependent output variables and structured output spaces. We propose to generalize multiclass Support Vector Machine learning in a formulation that involves features extracted jointly from inputs and… CONTINUE READING
Highly Influential
This paper has highly influenced 254 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 1,564 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 872 extracted citations

CMU: Arc-Factored, Discriminative Semantic Dependency Parsing

SemEval@COLING • 2014
View 9 Excerpts
Highly Influenced

Efficient semantic image segmentation with multi-class ranking prior

Computer Vision and Image Understanding • 2014
View 10 Excerpts
Highly Influenced

Relating Things and Stuff via ObjectProperty Interactions

IEEE Transactions on Pattern Analysis and Machine Intelligence • 2014
View 4 Excerpts
Highly Influenced

1,565 Citations

050100150'03'06'10'14'18
Citations per Year
Semantic Scholar estimates that this publication has 1,565 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.

Similar Papers

Loading similar papers…