O2 Muu julkaisu
Efficient optimization approaches for pairwise ranking losses
Tekijät: Antti Airola
Julkaisuvuosi: 2014
Journal: Dagstuhl Reports
Vuosikerta: 4
Numero: 3
Aloitussivu: 8
Lopetussivu: 8
ISSN: 2192-5283
DOI: https://doi.org/10.4230/DagRep.4.3.1
Verkko-osoite: http://drops.dagstuhl.de/opus/volltexte/2014/4550/
Tiivistelmä
Straightforward approaches to minimizing pairwise ranking losses on scored data lead to quadratic costs. We demonstrate, that for the special cases of pairwise hinge loss (RankSVM) and pairwise least-squares loss (RankRLS), better scaling can be achieved by modeling the preferences only implicitly using suitable data structures. Software implementations are available at http://staff.cs.utu.fi/~aatapa/software/RankSVM/(RankSVM) and https://github.com/aatapa/RLScore(RankRLS)
Straightforward approaches to minimizing pairwise ranking losses on scored data lead to quadratic costs. We demonstrate, that for the special cases of pairwise hinge loss (RankSVM) and pairwise least-squares loss (RankRLS), better scaling can be achieved by modeling the preferences only implicitly using suitable data structures. Software implementations are available at http://staff.cs.utu.fi/~aatapa/software/RankSVM/(RankSVM) and https://github.com/aatapa/RLScore(RankRLS)
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