Smoothed emphasis for boosting ensembles

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

Volumn 7902 LNCS, Issue PART 1, 2013, Pages 367-375

  Ahachad, Anas ^a   Omari, Adil ^a   Figueiras Vidal, Aníbal R ^a  

^a UNIVERSIDAD CARLOS III DE MADRID   (Spain)

Author keywords

Boosting; Classification; Emphasized samples; Machine ensembles; Nearest neighbors

Indexed keywords

BOOSTING; BOOSTING ENSEMBLES; NEAREST NEIGHBORS; SIMPLE MODIFICATIONS;

CLASSIFICATION (OF INFORMATION); NEURAL NETWORKS;

ADAPTIVE BOOSTING;

EID: 84880051653     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-38679-4_36     Document Type: Conference Paper

References (23)

1. Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
2. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)
3. Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley, Hoboken (2004)
4. Rokach, L.: Pattern Classification Using Ensemble Methods. World Scientific, Singapore (2010)
5. Schapire, R.E.: The strength of weak learnability. Machine Learning 5, 197-227 (1990)
6. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Computer and System Sciences 55(1), 119-139 (1997)
7. Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Machine Learning 37(3), 297-336 (1999)
8. Freund, Y.: An adaptive version of the boost by majority algorithm. Machine Learning 43(3), 293-318 (2001)
9. Gómez-Verdejo, V., Ortega-Moral, M., Arenas-García, J., Figueiras-Vidal, A.R.: Boosting by weighting critical and erroneous samples. Neurocomputing 69(7-9), 679-685 (2006)
10. Gómez-Verdejo, V., Arenas-García, J., Figueiras-Vidal, A.R.: A dynamically adjusted mixed emphasis method for building boosting ensembles. IEEE Trans. Neural Networks 19(1), 3-17 (2008)
11. Rätsch, G., Onoda, T., Müller, K.R.: Regularizing Adaboost. In: Kears, M., Solla, S., Cohn, D. (eds.) Advances in Neural Information Processing Systems, vol. 11, pp. 564-570. Cambridge University Press, Cambridge (1999)
12. Rätsch, G., Onoda, T., Müller, K.R.: Soft margins for AdaBoost. Machine Learning 42(3), 287-320 (2001)
13. Rätsch, G., Warmuth, M.K.: Efficient margin maximizing with boosting. Journal of Machine Learning Research 6, 2131-2152 (2005)
14. Sun, Y., Todorovic, S., Li, J.: Reducing the overfitting of AdaBoost by controlling its data distribution skewness. International Journal of Pattern Recognition and Artificial Intelligence 20(7), 1093-1116 (2006)
15. Shen, C., Li, H.: Boosting through optimization of margin distributions. IEEE Trans. Neural Networks 21(4), 659-666 (2010)
16. Zhang, C.-X., Zhang, J.-S., Zhang, G.-Y.: An efficient modified boosting method for solving classification problems. J. Computational and Applied Mathematics 214(2), 381-392 (2008)
17. Kim, H.C., Pang, S., Je, H.M., Kim, D., Bang, S.Y.: Constructing support vector machine ensemble. Pattern Recognition 26, 2757-2767 (2003)
18. Li, X., Wang, L., Sung, E.: Adaboost with SVM-based component classifiers. Engineering Applications of Artificial Intelligence 21, 785-795 (2008)
19. Mayhua-López, E., Gómez-Verdejo, V., Figueiras-Vidal, A.R.: Boosting ensembles with subsampling LPSVM learners. Submitted to IEEE Trans. Neural Networks and Machine Learning
20. Mayhua-López, E., Gómez-Verdejo, V., Figueiras-Vidal, A.R.: Real Adaboost with gate controlled fusion. IEEE Trans. Neural Networks and Learning Systems 23(12), 2003-2009 (2012)
21. UCI Machine Learning Repository. School Information & Computer Sciences, Univ. California, Irvine, http://archive.ics.uci.edu/ml
22. Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge (1996)
23. Kwok, J.T.Y.: Moderating the outputs of support vector machine classifiers. IEEE Trans. Neural Networks 10(5), 1018-1031 (1999)