INFORMAZIONI SU QUESTO ARTICOLO

Cita

Bala, M. and Agrawal, R.K. (2011). Optimal decision tree based multi-class support vector machine, Informatica 35(2): 197-209.Search in Google Scholar

Bartlett, P.L. and Shawe-Taylor, J. (1999). Generalization performance of support vector machines and other pattern classifiers, in B. Schölkopf et al. (Eds.), Advances in Kernel Methods, MIT Press, Cambridge, MA, pp. 43-54.10.7551/mitpress/1130.003.0007Search in Google Scholar

Blake, C.L. and Merz, C.J. (1998). UCI Repository of Machine Learning Databases, University of California, Irvine, CA, http://archive.ics.uci.edu/ml/.Search in Google Scholar

Bredensteiner, E.J. and Bennett, K.P. (1999). Multicategory classification by support vector machines, Computational Optimization 12(1-3): 53-79.10.1023/A:1008663629662Search in Google Scholar

Burges, C.J.C. (1998). A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery 2(2): 121-167.10.1023/A:1009715923555Search in Google Scholar

Chen, J., Wang, C. and Wang, R. (2009). Adaptive binary tree for fast SVM multiclass classification, Neurocomputing 72(13-15): 3370-3375.10.1016/j.neucom.2009.03.013Search in Google Scholar

Cheong, S., Hoon Oh, S. and Lee, S.-Y. (2004). Support vector machines with binary tree architecture for multi-class classification, Neural Information Processing Letters 2(3): 47-51.Search in Google Scholar

Chmielnicki, W. and Sta˛por, K. (2016). Using the one-versus-rest strategy with samples balancing to improve pairwise coupling classification, International Journal of Applied Mathematics and Computer Science 26(1): 191-201, DOI: 10.1515/amcs-2016-0013.10.1515/amcs-2016-0013Open DOISearch in Google Scholar

Crammer, K. and Singer, Y. (2002). On the learnability and design of output codes for multiclass problems, Machine Learning 47(2-3): 201-233.10.1023/A:1013637720281Search in Google Scholar

Dong, C., Zhou, B. and Hu, J. (2015). A hierarchical SVMbased multiclass classification by using similarity clustering, International Joint Conference on Neural Networks, Killarney, Ireland, pp.1-6.10.1109/IJCNN.2015.7280489Search in Google Scholar

Fei, B. and Liu, J. (2006). Binary tree of SVM: A new fast multiclass training and classification algorithm, IEEE Transactions on Neural Networks 17(3): 696-704.10.1109/TNN.2006.87234316722173Search in Google Scholar

Friedman, J. (1996). Another approach to polychotomous classification, Technical report, Stanford University, Stanford, CA.Search in Google Scholar

García, S., Fernández, A., Luengo, J. and Herrera, F. (2010). Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power, Information Sciences 180(10): 2044-2064.10.1016/j.ins.2009.12.010Search in Google Scholar

Hastie, T. and Tibshirani, R. (1998). Classification by pairwise coupling, Annals of Statistics 26(2): 451-471.10.1214/aos/1028144844Search in Google Scholar

Hsu, C. and Lin, C. (2002). A comparison of methods for multiclass support vector machines, IEEE Transactions on Neural Networks 13(2): 415-425.10.1109/72.99142718244442Search in Google Scholar

Joachims, T. (1999). Making large-scale SVM learning practical, in B. Schölkopf et al. (Eds.), Advances in Kernel Methods-Support Vector Learning, MIT Press, Cambridge, MA.Search in Google Scholar

Kijsirikul, B., Ussivakulz, N. and Road, P. (2002). Multiclass support vector machines using adaptive directed acyclic graph, International Joint Conference on Neural Networks, Honolulu, HI, USA, pp. 980-985.Search in Google Scholar

Knerr, S., Personnaz, L. and Dreyfus, G. (1990). Single-layer learning revisited: A stepwise procedure for building and training a neural network, Neurocomputing 68(68): 41-50.10.1007/978-3-642-76153-9_5Search in Google Scholar

Kumar, M.A. and Gopal, M. (2010). Fast multiclass SVM classification using decision tree based one-against-all method, Neural Processing Letters 32(3): 311-323.10.1007/s11063-010-9160-ySearch in Google Scholar

Kumar, M.A. and Gopal, M. (2011). Reduced one-against-all method for multiclass svm classification, Expert Systems with Applications 38(11): 14238-14248.Search in Google Scholar

Lei, H. and Govindaraju, V. (2005). Half-against-halfmulti-class support vector machines, in N.C. Oza et al. (Eds.), Multiple Classifier Systems, MCS 2005, Lecture Notes in Computer Science, Vol. 3541, Springer, Berlin/Heidelberg, pp. 156-164.10.1007/11494683_16Search in Google Scholar

Liu, B., Cao, L., Yu, P.S. and Zhang, C. (2008). Multi-space-mapped SVMs for multi-class classification, Proceedings of 8th IEEE International Conference on Data Mining, Washington, DC, USA, Vol. 8, pp. 911-916.10.1109/ICDM.2008.13Search in Google Scholar

Madzarov, G., Gjorgjevikj, D. and Chorbev, I. (2009). A multi-class SVM classifier utilizing binary decision tree support vector machines for pattern recognition, Electrical Engineering 33(1): 233-241.Search in Google Scholar

Platt, J., Cristianini, N. and Shawe-Taylor, J. (2000). Large margin DAGs for multiclass classification, in S.A. Solla et al. (Eds.), Advances in Neural Information Processing Systems, MIT Press, Cambridge, MA, pp. 547-553.Search in Google Scholar

Songsiri, P., Kijsirikul, B. and Phetkaew, T. (2008). Information-based dicrotomizer: A method for multiclass support vector machines, IEEE International Joint Conference on Neural Networks, Hong Kong, China, pp. 3284-3291.10.1109/IJCNN.2008.4634264Search in Google Scholar

Songsiri, P., Phetkaew, T. and Kijsirikul, B. (2015). Enhancement of multi-class support vector machine construction from binary learners using generalization performance, Neurocomputing 151(P1): 434-448.10.1016/j.neucom.2014.09.021Search in Google Scholar

Takahashi, F. and Abe, S. (2002). Decision-tree-based multiclass support vector machines, Proceedings of the 9th International Conference on Neural Information Processing, ICONIP’02, Singapore, Singapore, Vol. 3, pp. 1418-1488.Search in Google Scholar

Vapnik, V.N. (1998). Statistical Learning Theory, John Wiley & Sons, New York, NY.Search in Google Scholar

Vapnik, V.N. (1999). An overview of statistical learning theory, IEEE Transactions on Neural Networks 10(5): 988-99. Vapnik V.N., C.A. (1974). Teoriya Raspoznavaniya Obrazov: Statisticheskie Problemy Obucheniya (Theory of Pattern Recognition: Statistical Problems of Learning), Nauka, Moscow.10.1109/72.788640Search in Google Scholar

Yang, X., Yu, Q., He, L. and Guo, T. (2013). The one-against-all partition based binary tree support vector machine algorithms for multi-class classification, Neurocomputing 113(3): 1-7.10.1016/j.neucom.2012.12.048Search in Google Scholar

eISSN:
2083-8492
Lingua:
Inglese
Frequenza di pubblicazione:
4 volte all'anno
Argomenti della rivista:
Mathematics, Applied Mathematics