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Learning layered ranking functions with structured support vector machines

Willem Waegeman (UGent) , Bernard De Baets (UGent) and Luc Boullart (UGent)
(2008) NEURAL NETWORKS. 21(10). p.1511-1523
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Keywords
Ordinal regression, Layered ranking, ROC analysis, Graph representations of rank data, Structured learning, Quadratic programming, Kernel methods

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Citation

Please use this url to cite or link to this publication:

MLA
Waegeman, Willem, Bernard De Baets, and Luc Boullart. “Learning Layered Ranking Functions with Structured Support Vector Machines.” NEURAL NETWORKS 21.10 (2008): 1511–1523. Print.
APA
Waegeman, W., De Baets, B., & Boullart, L. (2008). Learning layered ranking functions with structured support vector machines. NEURAL NETWORKS, 21(10), 1511–1523.
Chicago author-date
Waegeman, Willem, Bernard De Baets, and Luc Boullart. 2008. “Learning Layered Ranking Functions with Structured Support Vector Machines.” Neural Networks 21 (10): 1511–1523.
Chicago author-date (all authors)
Waegeman, Willem, Bernard De Baets, and Luc Boullart. 2008. “Learning Layered Ranking Functions with Structured Support Vector Machines.” Neural Networks 21 (10): 1511–1523.
Vancouver
1.
Waegeman W, De Baets B, Boullart L. Learning layered ranking functions with structured support vector machines. NEURAL NETWORKS. 2008;21(10):1511–23.
IEEE
[1]
W. Waegeman, B. De Baets, and L. Boullart, “Learning layered ranking functions with structured support vector machines,” NEURAL NETWORKS, vol. 21, no. 10, pp. 1511–1523, 2008.
@article{526874,
  author       = {Waegeman, Willem and De Baets, Bernard and Boullart, Luc},
  issn         = {0893-6080},
  journal      = {NEURAL NETWORKS},
  keywords     = {Ordinal regression,Layered ranking,ROC analysis,Graph representations of rank data,Structured learning,Quadratic programming,Kernel methods},
  language     = {eng},
  number       = {10},
  pages        = {1511--1523},
  title        = {Learning layered ranking functions with structured support vector machines},
  url          = {http://dx.doi.org/10.1016/j.neunet.2008.07.008},
  volume       = {21},
  year         = {2008},
}

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