Submodular multi-label learning

Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011

Volumn , Issue , 2011, Pages

  Petterson, James ^a   Caetano, Tiberio ^a  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); COMBINATORIAL OPTIMIZATION; LEARNING SYSTEMS;

CO-OCURRENCES; F-SCORE; GRAPH-CUT; LEARN+; MULTI-LABEL LEARNING; MULTI-LABELS; PAIRWISE LABELS; QUALITY OF PREDICTIONS; SUBMODULAR; SUBMODULAR SET FUNCTIONS;

GRAPHIC METHODS;

EID: 85162354773     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. K. Dembczynski, W. Cheng, and E. Hüllermeier, "Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains," in ICML, 2010.
2. X. Zhang, T. Graepel, and R. Herbrich, "Bayesian Online Learning for Multi-label and Multi-variate Performance Measures," in AISTATS, 2010.
3. P. Rai and H. Daume, "Multi-Label Prediction via Sparse Infinite CCA," in NIPS, 2009.
4. J. Read, B. Pfahringer, G. Holmes, and E. Frank, "Classifier chains for multi-label classification.," in ECML/PKDD, 2009.
5. J. Rousu, C. Saunders, S. Szedmak, and J. Shawe-Taylor, "Kernel-based learning of hierarchical multilabel classification models," JMLR, vol. 7, pp. 1601-1626, December 2006.
6. Z. Barutcuoglu, R. E. Schapire, and O. G. Troyanskaya, "Hierarchical multi-label prediction of gene function," Bioinformatics, vol. 22, pp. 830-836, April 2006.
7. M. Guillaumin, T. Mensink, J. Verbeek, and C. Schmid, "TagProp: Discriminative Metric Learning in Nearest Neighbor Models for Image Auto-Annotation," in ICCV, 2009.
8. M. Jansche, "Maximum expected F-measure training of logistic regression models," HLT, 2005.
9. T. Mensink, J. Verbeek, and G. Csurka, "Learning structured prediction models for interactive image labeling," in CVPR, 2011.
10. I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun, "Large margin methods for structured and interdependent output variables," JMLR, vol. 6, pp. 1453-1484, 2005.
11. J. Petterson and T. Caetano, "Reverse multi-label learning," in NIPS, 2010.
12. W. Bi and J. Kwok, "Multi-Label Classification on Tree- and DAG-Structured Hierarchies," in ICML, 2011.
13. N. Ghamrawi and A. Mccallum, "Collective Multi-Label Classification," 2005.
14. B. Hariharan, S. V. N. Vishwanathan, and M. Varma, "Large Scale Max-Margin Multi-Label Classification with Prior Knowledge about Densely Correlated Labels," in ICML, 2010.
15. G. Tsoumakas, I. Katakis, and I. P. Vlahavas, Mining Multi-label Data. Springer, 2009.
16. G. Tsoumakas and I. P. Vlahavas, "Random k-labelsets: An ensemble method for multilabel classification," in ECML, 2007.
17. S. Virtanen, A. Klami, and S. Kaski, "Bayesian CCA via Group Sparsity," in ICML, 2011.
18. C. H. Teo, S. V. N. Vishwanathan, A. J. Smola, and Q. V. Le, "Bundle methods for regularized risk minimization," JMLR, vol. 11, pp. 311-365, 2010.
19. Z. Svitkina and L. Fleischer, "Submodular approximation: Sampling-based algorithms and lower bounds," in FOCS, 2008.
20. M.-L. Zhang and Z.-H. Zhou, "ML-KNN: A lazy learning approach to multi-label learning," Pattern Recognition, vol. 40, pp. 2038-2048, July 2007.
21. Y. Boykov and V. Kolmogorov, "An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision," IEEE Trans. PAMI, 2004.