Probabilistic score estimation with piecewise logistic regression

Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004

Volumn , Issue , 2004, Pages 911-918

  Zhang, Jian ^a   Yang, Yiming ^a  

^a Carnegie Mellon University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CALIBRATION; DECISION MAKING; ESTIMATION; HIERARCHICAL SYSTEMS; LAPLACE TRANSFORMS; METRIC SYSTEM; PIECEWISE LINEAR TECHNIQUES; REGRESSION ANALYSIS; SAMPLING;

LOGISTIC REGRESSION CLASSIFIER; NON-DETERMINISTIC DECISIONS; PIECEWISE LOGISTIC REGRESSION; PROBABILISTIC SCORE ESTIMATION;

PROBABILITY;

EID: 14344263881     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Bennett, P. (2003). Using Asymmetric Distributions to Improve Text Classifier Probability Estimates. Proceedings of SIGIR'03.
2. Domingos, P., & Pazzani, M. (1996). Beyond independence: Conditions for the optimally of the simple bayesian classifier. ICML'96.
3. Duda, R., Hart, P., & Stork, D. (2001). Pattern Classification. John Wiley & Sons, Inc., 2001.
4. Hastie, T., & Tibshirani, R. (1996). Generalized Additive Model. Statistical Sciences, vol 1:297-318.
5. Hastie., T., & Tibshirani, R. (1996). Classification by pairwise coupling. Technical Report, Stanford University and University of Toronto.
6. Joachims, T. (1998a). Text categorization with support vector machines: Learning with many relevant features. Proceedings of ECML.
7. Kotz, S., Kozubowski T., & Podgorski K. (2001). The Laplace Distribution and Generalizations: A Revisit with Applications to Communications, Economics, Engineering, and Finance. Brikhauser.
8. Lewis, D., Schapire, R., Callan, J., & Papka, R. (1996). Training algorithms for linear text classifiers. Proceedings of SIGIR'96.
9. Lewis, D., & Gale, W. (1994). A sequential algorithm for training text classifiers. SIGIR94.
10. McCallum, A. (1996). Bow: A toolkit for statistical language modeling, text retrieval, classification and clustering, http://www.cs.cmu.edu/~mccallum/ bow.
11. McCallum, A., & Nigam, K. (1998). A comparison of event models for naive bayes text classification. AAAI'98 Workshop on Learning for Text Categorization.
12. Ng, A., & Jordan M. (2002). On Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes. Proceedings of NIPS 14.
13. Platt. J. (1999). Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods. Advances in Large Margin Classifiers, MIT Press.
14. Rubinstein, Y., & Hastie, T. (1997). Discriminative vs. informative learning.p Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, pages 49-53. AAAI Press, 1997.
15. Vapnik, V. (1999). The Nature of Statistical Learning Theory, 2nd edition. Springer Verlag.
16. Yang, Y., & Pedersen, J. (1997). A Comparative Study of Feature Selection in Text Categorization. ICML97.
17. Zadrozny, B., & Elkan, C. (2001). Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers. Proceedings of the Eighteenth International Conference on Machine Learning, 2001.
18. Zadrozny, B., & Elkan, C. (2002). Transforming Classifier Scores into Accurate Multiclass Probability Estimates. SIGKDD, 2002.
19. Zhang, T., & Oles, F. (2001). Text Categorization based on regularized linear classification methods. Information Retrieval, vol 4:5-31.