A4 Vertaisarvioitu artikkeli konferenssijulkaisussa
An Improved Training Algorithm for the Linear Ranking Support Vector Machine
Tekijät: Airola A, Pahikkala T, Salakoski T
Julkaisuvuosi: 2011
Journal: Lecture Notes in Computer Science
Tietokannassa oleva lehden nimi: ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2011, PT I
Lehden akronyymi: LECT NOTES COMPUT SC
Vuosikerta: 6791
Aloitussivu: 134
Lopetussivu: 141
Sivujen määrä: 8
ISBN: 978-3-642-21734-0
ISSN: 0302-9743
Tiivistelmä
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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