Semi-supervised learning from only positive and unlabeled data using entropy

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

Volumn 6184 LNCS, Issue , 2010, Pages 668-679

  Wang, Xiaoling ^a   Xu, Zhen ^b   Sha, Chaofeng ^b   Ester, Martin ^c   Zhou, Aoying ^a,b  

^a EAST CHINA NORMAL UNIVERSITY   (China)

^b FUDAN UNIVERSITY   (China)

^c SIMON FRASER UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

LOGISTIC REGRESSION CLASSIFIER; NEGATIVE EXAMPLES; SEMI-SUPERVISED LEARNING; TRAINING SETS; UNLABELED DATA;

CLASSIFICATION (OF INFORMATION); SUPERVISED LEARNING;

INFORMATION MANAGEMENT;

EID: 77955031833     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-14246-8_64     Document Type: Conference Paper

References (16)

1. Manevitz, L.M., Yousef, M., Cristianini, N., Shawe-taylor, J., Williamson, B.: Oneclass svms for document classification. Journal of Machine Learning Research 2, 139-154 (2001)
2. Yu, H., Han, J., Chang, K.C.C.: Pebl: Positive example based learning for web page classification using svm. In: KDD (2002)
3. Li, X., Liu, B.: Learning to classify texts using positive and unlabeled data. In: IJCAI (2003)
4. Li, X., Liu, B., Ng, S.K.: Learning to identify unexpected instances in the test set. In: IJCAI (2007)
5. Denis, F.: PAC learning from positive statistical queries. In: Richter, M.M., Smith, C.H., Wiehagen, R., Zeugmann, T. (eds.) ALT 1998. LNCS (LNAI), vol. 1501, pp. 112-126. Springer, Heidelberg (1998)
6. Cortes, C., Vapnik, V.: Support vector networks. Machine Learning 20, 273-297 (1995)
7. Zhang, D., Lee, W.S.: A simple probabilistic approach to learning from positive and unlabeled examples. In: UKCI (2005)
8. Elkan, C., Noto, K.: Learing classifiers from only positive and unlabeled data. In: KDD (2008)
9. Cover, T., Thomas, J.: Elements of Information Theory. Wiley Interscience, Hoboken (1991)
10. Guo, Y., Greiner, R.: Optimistic active learning using mutual information. In: IJCAI (2007)
11. Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
12. Le Cessie, S., Van Houwelingen, J.C.: Ridge estimators in logistic regression. Applied Statistics 41, 191-201 (1997)
13. Sumner, M., Frank, E., Hall, M.: Speeding up logistic model tree induction. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 675-683. Springer, Heidelberg (2005)
14. Landwehr, N., Hall, M., Frank, E.: Logistic model trees. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) ECML 2003. LNCS (LNAI), vol. 2837, pp. 241-252. Springer, Heidelberg (2003)
15. Sha, C., Xu, Z., Wang, X., Zhou, A.: Directly identify unexpected instances in the test set by entropy maximization. In: APWEB-WAIM (2009)
16. Duffy, D.E., Samtmer, T.J.: On the small sample properties of norm-restricted maximum likelihood estimators for logistic regression models. In: Communs Statist. Theory Meth. (1989)