Difference between revisions of "Tsochantaridis et al, JMLR 2005"

From Cohen Courses
Jump to navigationJump to search
Line 3: Line 3:
 
== Citation ==
 
== Citation ==
  
{{MyCiteconference | booktitle = Journal of Machine Learning Research | coauthors = I. Tsochantaridis, T. Joachims, T. Hofmann, Y. Altun | date = 2005 | first = I| last = Tsochantaridis | pages = 1453-1484| title =  
+
{{MyCiteconference | booktitle = Journal of Machine Learning Research | coauthors = T. Joachims, T. Hofmann, Y. Altun | date = 2005 | first = I| last = Tsochantaridis | pages = 1453-1484| title =  
 
Large Margin Methods for Structured and Interdependent Output Variables | url = http://jmlr.csail.mit.edu/papers/volume6/tsochantaridis05a/tsochantaridis05a.pdf }}
 
Large Margin Methods for Structured and Interdependent Output Variables | url = http://jmlr.csail.mit.edu/papers/volume6/tsochantaridis05a/tsochantaridis05a.pdf }}
  

Revision as of 14:22, 24 September 2011

UNDER CONSTRUCTION

Citation

Large Margin Methods for Structured and Interdependent Output Variables, by I Tsochantaridis, T. Joachims, T. Hofmann, Y. Altun. In Journal of Machine Learning Research, 2005.

This Paper can be found online [1].

Summary

This paper deals with the problem of learning a mapping from input vectors to discrete variables . However, is not a finite set of labels (as in multiclass classification) or a real number (as in regression). Instead, is a set of structured objects (such as trees, sequences, graphs, strings, etc).

Brief description of the method

Experimental Result

Related papers