Training linear ranking SVMs in linearithmic time using red-black trees

Pattern Recognition Letters

Volumn 32, Issue 9, 2011, Pages 1328-1336

  Airola, Antti ^a,b   Pahikkala, Tapio ^a,b   Salakoski, Tapio ^a,b  

^a UNIVERSITY OF TURKU   (Finland)

^b TURKU CENTRE FOR COMPUTER SCIENCE   (Finland)

Author keywords

Bundle methods; Cutting plane method; Learning to rank; Ranking support vector machine; Red black tree

Indexed keywords

BUNDLE METHODS; CUTTING PLANE METHOD; LEARNING TO RANK; RANKING SUPPORT VECTOR MACHINE; RED BLACK TREE;

SUPPORT VECTOR MACHINES;

EID: 79953865926     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2011.03.014     Document Type: Conference Paper

References (32)

1. R. Bayer Symmetric binary B-trees: Data structure and maintenance algorithms Acta Inform. 1 4 1972 290 306
2. L. Bottou, and C.-J. Lin Support vector machine solvers L. Bottou, O. Chapelle, D. DeCoste, J. Weston, Large-Scale Kernel Machines Neural Information Processing 2007 MIT Press Cambridge, MA, USA 1 28
3. A.P. Bradley The use of the area under the ROC curve in the evaluation of machine learning algorithms Pattern Recognition 30 7 1997 1145 1159 (Pubitemid 127406521)
4. C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender Learning to rank using gradient descent L.D. Raedt, S. Wrobel, Proc, 22nd Internat. Conf. on Machine learning (ICML 2005) ACM International Conference Proceeding Series vol. 119 2005 ACM New York, NY, USA 89 96 (Pubitemid 43183320)
5. O. Chapelle Training a support vector machine in the primal Neural Comput. 19 5 2007 1155 1178
6. O. Chapelle, and S.S. Keerthi Efficient algorithms for ranking with SVMs Inform. Retrieval 13 3 2010 201 215
7. T.H. Cormen, C.E. Leiserson, R.L. Rivest, and C. Stein Introduction to Algorithms second ed. 2001 MIT Press Cambridge, MA, USA
8. C. Cortes, and M. Mohri AUC optimization vs. error rate minimization S. Thrun, L. Saul, B. Schölkopf, Advances in Neural Information Processing Systems vol. 16 2004 MIT Press Cambridge, MA, USA
9. C. Cortes, and V. Vapnik Support-vector networks Mach. Learn. 20 3 1995 273 297
10. H. Drucker, C.J. Burges, L. Kaufman, A. Smola, and V. Vapnik Support vector regression machines M. Mozer, M. Jordan, T. Petsche, Advances in Neural Information Processing Systems vol. 9 1997 MIT Press Cambridge, MA, USA 155 161
11. V. Franc, and S. Sonnenburg Optimized cutting plane algorithm for large-scale risk minimization J. Mach. Learn. Res. 10 2009 2157 2192
12. Y. Freund, R. Iyer, R.E. Schapire, and Y. Singer An efficient boosting algorithm for combining preferences J. Mach. Learn. Res. 4 2003 933 969
13. J. Fürnkranz, and E. Hüllermeier Preference Learning 1st ed. 2011 Springer Berlin, Heidelberg, Germany
14. J. Fürnkranz, and E. Hüllermeier Preference learning Künstliche Intelligenz 19 1 2005 60 61
15. J.A. Hanley, and B.J. McNeil The meaning and use of the area under a receiver operating characteristic (ROC) curve Radiology 143 1 1982 29 36 (Pubitemid 12142173)
16. R. Herbrich, T. Graepel, and K. Obermayer Support vector learning for ordinal regression D. Willshaw, A. Murray, Proc. 9th Internat. Conf. on Artificial Neural Networks (ICANN 1999) 1999 Institute of Electrical Engineers London 97 102 (Pubitemid 30519921)
17. T. Joachims Optimizing search engines using clickthrough data D. Hand, D. Keim, R. Ng, Proc. 8th ACM SIGKDD Conf. on Knowledge Discovery and Data Mining (KDD 2002) 2002 ACM Press New York, NY, USA 133 142
18. T. Joachims A support vector method for multivariate performance measures L.D. Raedt, S. Wrobel, Proc. 22nd Internat. Conf. on Machine learning (ICML 2005) ACM International Conference Proceeding Series vol. 119 2005 ACM Press New York, NY, USA 377 384 (Pubitemid 43183356)
19. T. Joachims Training linear SVMs in linear time T. Eliassi-Rad, L.H. Ungar, M. Craven, D. Gunopulos, Proc. 12th ACM SIGKDD Internat. Conf. on Knowledge Discovery and Data Mining (KDD 2006) 2006 ACM Press New York, NY, USA 217 226 (Pubitemid 44535518)
20. T. Joachims, and C.-N.J. Yu Sparse kernel SVMs via cutting-plane training Mach. Learn. 76 2-3 2009 179 193
21. D.D. Lewis, Y. Yang, T.G. Rose, and F. Li RCV1: A new benchmark collection for text categorization research J. Mach. Learn. Res. 5 2004 361 397
22. T.-Y. Liu Learning to rank for information retrieval Found. Trend Inform. Retrieval 3 3 2009 225 331
23. Pahikkala, T., Tsivtsivadze, E., Airola, A., Boberg, J., Salakoski, T., 2007. Learning to rank with pairwise regularized least-squares. In: Joachims, T., Li, H., Liu, T.-Y., Zhai, C., (Eds.), SIGIR 2007 Workshop on Learning to Rank for Information Retrieval, pp. 27-33.
24. T. Pahikkala, E. Tsivtsivadze, A. Airola, J. Boberg, and J. Järvinen An efficient algorithm for learning to rank from preference graphs Mach. Learn. 75 1 2009 129 165
25. T. Poggio, and F. Girosi Networks for approximation and learning Proc. IEEE 78 9 1990
26. F.J. Provost, T. Fawcett, and R. Kohavi The case against accuracy estimation for comparing induction algorithms J. Shavlik, Proc. 15th Internat. Conf. on Machine Learning (ICML 1998) 1998 Morgan Kaufmann Publishers Inc. San Francisco, CA, USA 445 453
27. A.J. Smola, S.V.N. Vishwanathan, and Q. Le Bundle methods for machine learning A. McCallum, Advances in Neural Information Processing Systems vol. 20 2007 MIT Press Cambridge, MA, USA
28. C.H. Teo, A. Smola, S.V. Vishwanathan, and Q.V. Le A scalable modular convex solver for regularized risk minimization P. Berkhin, R. Caruana, X. Wu, S. Gaffney, Proc. 13th ACM SIGKDD Internat. Conf. on Knowledge Discovery and Data Mining (KDD 2007) 2007 ACM New York, NY, USA 727 736 (Pubitemid 350229258)
29. C.H. Teo, S.V. Vishwanathan, A. Smola, and Q.V. Le Bundle methods for regularized risk minimization J. Mach. Learn. Res. 11 2010 311 365
30. I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun Large margin methods for structured and interdependent output variables J. Mach. Learn. Res. 6 2005 1453 1484
31. W. Waegeman, B. De Baets, and L. Boullart ROC analysis in ordinal regression learning Pattern Recognition Lett. 29 1 2008 1 9 (Pubitemid 350026657)
32. J.W.J. Williams Algorithm 232 - Heapsort Commun. ACM 7 6 1964 347 348