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| − | == UNDER CONSTRUCTION ==
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| − | == Citation ==
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| − | {{MyCiteconference | booktitle = Journal of Machine Learning Research | coauthors = T. Joachims, T. Hofmann, Y. Altun | date = 2005 | first = I| last = Tsochantaridis | pages = 1453-1484| title =
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| − | Large Margin Methods for Structured and Interdependent Output Variables | url = http://jmlr.csail.mit.edu/papers/volume6/tsochantaridis05a/tsochantaridis05a.pdf }}
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| − | This [[Category::Paper]] can be found online [http://jmlr.csail.mit.edu/papers/volume6/tsochantaridis05a/tsochantaridis05a.pdf].
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| − | == Summary ==
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| − | This paper deals with the problem of learning a mapping from input vectors <math>x\in\mathcal{X}</math> to discrete variables <math>y\in\mathcal{Y}</math>. However, <math>\mathcal{Y}</math> is not a finite set of labels (as in multiclass classification) or a real number (as in regression). Instead, <math>\mathcal{Y}</math> is a set of structured objects (such as trees, sequences, graphs, strings, etc).
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| − | == Brief description of the method ==
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| − | == Experimental Result ==
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| − | == Related papers ==
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