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@TECHREPORT{jf:TUD-KE-2007-05,
       author = {Loza Menc{\'{\i}}a, Eneldo and F{\"{u}}rnkranz, Johannes},
        title = {Pairwise Learning of Multilabel Classifications with Perceptrons},
       number = {TUD-KE-2007-05},
         year = {2007},
  institution = {TU Darmstadt, Knowledge Engineering Group},
          url = {http://www.ke.tu-darmstadt.de/publications/reports/tud-ke-2007-05.pdf},
     abstract = {Multiclass Multilabel Perceptrons (MMP) are an efficient incremental algorithm for
training a team of perceptrons for a multilabel prediction task. The key idea is to train
one binary classifier per label, as is typically done for addressing multilabel problems, but
to make the training signal dependent on the performance of the whole ensemble. In this
paper, we propose an alternative approach that is based on a pairwise approach, i.e., we
incrementally train a perceptron for each pair of classes. An evaluation on the Reuters
2000 (RCV1) data shows that our multilabel pairwise perceptron (MLPP) algorithm yields
substantial improvements over MMP in terms of ranking quality and overfitting resistance,
while maintaining its efficiency. Despite the quadratic increase in the number of perceptrons
that have to be trained, the increase in computational complexity is bounded by the average
number of labels per training example.}
}