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An Improved Training Algorithm for the Linear Ranking Support Vector Machine

Antti Airola, Tapio Pahikkala, Tapio Salakoski, An Improved Training Algorithm for the Linear Ranking Support Vector Machine. In: Timo Honkela, Wlodzislaw Duch, Mark Girolami, Samuel Kaski (Eds.), Proceedings of the 21st International Conference on Artificial Neural Networks (ICANN 2011), 6791, 134–141, Springer, 2011.

Abstract:

We introduce an O(m*s+m*log(m) time complexity method for training the linear ranking support vector machine, where m is the number of training examples, and s the average number of non-zero features per example. The method generalizes the fastest previously known approach, which achieves the same efficiency only in restricted special cases. The excellent scalability of the proposed method is demonstrated experimentally.

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BibTeX entry:

@INPROCEEDINGS{iAiPaSa11a,
  title = {An Improved Training Algorithm for the Linear Ranking Support Vector Machine},
  booktitle = {Proceedings of the 21st International Conference on Artificial Neural Networks (ICANN 2011)},
  author = {Airola, Antti and Pahikkala, Tapio and Salakoski, Tapio},
  volume = {6791},
  editor = {Honkela, Timo and Duch, Wlodzislaw and Girolami, Mark and Kaski, Samuel},
  publisher = {Springer},
  pages = {134–141},
  year = {2011},
  keywords = {binary search tree, cutting plane optimization, learning to rank, support vector machine},
}

Belongs to TUCS Research Unit(s): Algorithmics and Computational Intelligence Group (ACI), Turku BioNLP Group

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