Large-scale many-class learning

Society for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining 2008, Proceedings in Applied Mathematics 130

Volumn 2, Issue , 2008, Pages 846-857

  Madani, Omid ^a   Connor, Michael ^b  

^a SRI INTERNATIONAL   (United States)

^b UIUC

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING ALGORITHMS; SUPPORT VECTOR MACHINES; TEXT PROCESSING; VECTOR SPACES;

BIPARTITE GRAPHS; CATEGORY INDEX; EFFICIENT LEARNING; NUMBER OF CLASS; ORDERS OF MAGNITUDE; TEXT CATEGORIZATION; TOP-DOWN LEARNING; WORD PREDICTION;

DATA MINING;

EID: 52649182137     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972788.76     Document Type: Conference Paper

References (30)

1. W. W. Cohen and Y. Singer. Context-senstive learning methods for text categorization. In SIGIR, 1996.
2. K. Crammer, O Dekel, J. Keshet, S. Shalev-Shwartz, and Y. Singer. Online passive-aggressive algorithms. JMLR, 7, 2006.
3. K. Crammer and Y. Singer. A family of additive online algorithms for category ranking. JMLR, 3, 2003.
4. O. Dekel, J. Keshet, and Y. Singer. Large margin hierarchical classification. In ICML, 2003.
5. S. Dumais and H. Chen. Hierarchical classification of web content. In SIGIR, 2000.
6. Y. Even-Zohar and D. Roth. A classification approach to word prediction. In NAACL, 2000.
7. B. S. Everitt. Cambridge Dictionary of Statistics. Cambridge University Press, 2nd edition edition, 2003.
8. D. A. Forsyth and J. Ponce. Computer Vision. Prentice Hall, 2003.
9. Y. Freund, R. Schapire, Y. Singer, and M. Warmuth. Using and combining predictors that specialize. In STOC, 1997.
10. M. R. Garey and D. S. Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman, 1979.
11. C. Genest and J. V. Zidek. Combining probability distributions: A critique and an annotated bibliography. Statistical Science, 1(1): 114-148, 1986.
12. J. T. Goodman. A bit of progress in language modeling. Computer Speech and Language, 15(4):403-434, 2001.
13. K. Grill-Spector and N. Kanwisher. Visual recognition, as soon as you know it is there, you know what it is. Psychological Science, 16(2): 152-160, 2005.
14. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning. Springer-Verlag, 2001.
15. R. M. Karp, C. H. Papadimitriou, and S. Shenker. A simple algorithm for finding frequent elements in streams and bags. ACM Trans. Database Systems, 2003.
16. S. Keerthi and D. DeCoste. A modified finite Newton method for fast solution of large scale linear SVMs. JMLR, 2006.
17. D. Koller and M. Sahami. Hierarchically classifying documents using very few words. In ICML, 1997.
18. D. D. Lewis, Y. Yang, T. G. Rose, and F. Li. RCV1: A new benchmark collection for text categorization research. JMLR, 5:361-397, 2004.
19. T. Liu, Y. Yang, H. Wan, H. Zeng, Z. Chen, and W. Ma. Support vector machines classification with very large scale taxonomy. SIGKDD Explorations, 7, 2005.
20. O. Madani. Exploring massive learning via a prediction system. In AAAI Fall Symposium Series: Computational Approaches to Representation Change, 2007.
21. O. Madani. Prediction games in infinitely rich worlds. In AAAI Fall Symposium Series, 2007.
22. O. Madani and M. Connor. Ranked Recall: Efficient classification by efficient learning of indices that rank. Technical report, Yahoo! Research, 2007.
23. O. Madani, W. Greiner, D. Kempe, and M. Salavatipour. Recall systems: Efficient learning and use of category indices. In AISTATS, 2007.
24. C. Mesterharm. A multi-class linear learning algorithm related to Winnow. In NIPS, 2000.
25. J. Piatt. Probabilities for SVMs and comparisons to regularized likelihood methods. In Advances in Large Margin Classifiers. MIT Press, 1999.
26. J. Rennie, L. Shih, J. Teevan, and D. Karger. Tackling the poor assumption of Naive Bayes text classifiers. In ICML, 2003.
27. R. Rifkin and A. Klautau. In defense of one-vs-all classification. JMLR, 5, 2004.
28. F. Sebastiani. Machine learning in automated text categorization. ACMComputing Surveys, 2002.
29. S. Thorpe, D. Fize, and C. Marlot. Speed of processing in the human visual system. Nature, 381, 520-522.
30. J. Z. Wang, J. Li, and G. Wiederhold. SIMPLIcity: Semantics-sensitive integrated matching for picture libraries. IEEE PAMI, 23(9):947-963, 2001.