A4 Refereed article in a conference publication
An Improved Training Algorithm for the Linear Ranking Support Vector Machine
Authors: Airola A, Pahikkala T, Salakoski T
Publication year: 2011
Journal: Lecture Notes in Computer Science
Journal name in source: ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2011, PT I
Journal acronym: LECT NOTES COMPUT SC
Volume: 6791
First page : 134
Last page: 141
Number of pages: 8
ISBN: 978-3-642-21734-0
ISSN: 0302-9743
Abstract
We introduce an O(ms + m log(m)) time complexity method for training the linear ranking support vector machine, where in 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.
We introduce an O(ms + m log(m)) time complexity method for training the linear ranking support vector machine, where in 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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