Difference between revisions of "Tsochantaridis et al, JMLR 2005"
From Cohen Courses
Jump to navigationJump to searchLine 3: | Line 3: | ||
== Citation == | == Citation == | ||
− | {{MyCiteconference | booktitle = Journal of Machine Learning Research | coauthors = | + | {{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
Contents
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).