A1 Journal article – refereed
Training linear ranking SVMs in linearithmic time using red-black trees




List of Authors: Antti Airola, Tapio Pahikkala, Tapio Salakoski
Publisher: ELSEVIER SCIENCE BV
Publication year: 2011
Journal: Pattern Recognition Letters
Journal name in source: PATTERN RECOGNITION LETTERS
Journal acronym: PATTERN RECOGN LETT
Number in series: 11
Volume number: 32
Issue number: 9
ISSN: 0167-8655

Abstract


We introduce an efficient method for training the linear ranking support vector machine. The method combines cutting plane optimization with red-black tree based approach to subgradient calculations, and has O(ms + mlog (m)) time complexity, where m is the number of training examples, and s the average number of non-zero features per example. Best previously known training algorithms achieve the same efficiency only for restricted special cases, whereas the proposed approach allows any real valued utility scores in the training data. Experiments demonstrate the superior scalability of the proposed approach, when compared to the fastest existing RankSVM implementations.



Last updated on 2019-29-01 at 10:24