Learnability of bipartite ranking functions

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3559 LNAI, Issue , 2005, Pages 16-31

  Agarwal, Shivani ^a   Roth, Dan ^a  

^a University of Illinois at Urbana Champaign   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTATIONAL COMPLEXITY; FUNCTIONS; PROBLEM SOLVING;

CLASSIFICATION FUNCTIONS; COMBINATORIAL PARAMETERS; LEARNABILITY; REAL-VALUED RANKING FUNCTION;

LEARNING SYSTEMS;

EID: 26944498632     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11503415_2     Document Type: Conference Paper

References (20)

1. Valiant, L.G.: A theory of the learnable. Communications of the ACM (1984) 1134-1142
2. Cohen, W.W., Schapire, R.E., Singer, Y.: Learning to order things. Journal of Artificial Intelligence Research 10 (1999) 243-270
3. Herbrich, R., Graepel, T., Obermayer, K.: Large margin rank boundaries for ordinal regression. Advances in Large Margin Classifiers (2000) 115-132
4. Crammer, K., Singer, Y.: Pranking with ranking. In Dietterich, T.G., Becker, S., Ghahramani, Z., eds.: Advances in Neural Information Processing Systems 14, MIT Press (2002) 641-647
5. Freund, Y., Iyer, R., Schapire, R.E., Singer, Y.: An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research 4 (2003) 933-969
6. Blumer, A., Ehrenfeucht, A., Haussler, D., Warmuth, M.K.: Learnability and the Vapnik-Chervonenkis dimension. Journal of the ACM 36 (1989) 929-965
7. Vapnik, V.M., Chervonenkis, A.: On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability and its Applications 16 (1971) 264-280
8. Haussler, D.: Decision theoretic generalizations of the PAC model for neural net and other learning applications. Information and Computation 100 (1992) 78-150
9. Kearns, M.J., Schapire, R.E., Sellie, L.M.: Toward efficient agnostic learning. Machine Learning 17 (1994) 115-141
10. Pollard, D.: Convergence of Stochastic Processes. Springer-Verlag (1984)
11. Kearns, M.J., Schapire, R.E.: Efficient distribution-free learning of probabilistic concepts. Journal of Computer and System Sciences 48 (1994) 464-497
12. Alon, N., Ben-David, S., Cesa-Bianchi, N., Haussler, D.: Scale-sensitive dimensions, uniform convergence, and learnability. Journal of the ACM 44 (1997) 615-631
13. Bartlett, P.L., Long, P.M., Williamson, R.C.: Fat-shattering and the learnability of real-valued functions. Journal of Computer and System Sciences 52 (1996) 434-452
14. Agarwal, S., Graepel, T., Herbrich, R., Har-Peled, S., Roth, D.: Generalization bounds for the area under the ROC curve. Journal of Machine Learning Research (2005) 393-425
15. Cortes, C., Mohri, M.: AUC optimization vs. error rate minimization. In: Advances in Neural Information Processing Systems 16, MIT Press (2004)
16. Anthony, M., Bartlett, P.L.: Learning in Neural Networks: Theoretical Foundations. Cambridge University Press (1999)
17. Agarwal, S.: A Study of the Bipartite Ranking Problem in Machine Learning. PhD thesis, University of Illinois at Urbana-Champaign (2005)
18. Aho, A.V., Hopcroft, J.E., Ullman, J.D.: The Design and Analysis of Computer Algorithms. Addison-Wesley (1974)
19. Karmarkar, N.: A new polynomial-time algorithm for linear programming. Combinatorica 4 (1984) 373-395
20. Pitt, L., Valiant, L.G.: Computational limitations on learning from examples. Journal of the ACM 35 (1988) 965-984