참고문헌
- P. Ciudici, Applied Data Mining, Wiley, 2003
- J. Han, M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, 2001
- T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data mining, Inference, and Prediction, Springer, 2001
- S.R. Gunn, "Support Vector Machines for Classification and Regression", Technical Report, University of Southampton, 1998
- V. Cherkassky, F. Mulier, Learning From Data Concepts, Theory, and Methods, John Wiley & Sons, 1998
- J. H. Friedman, "An Overview of Predictive Learning and Function Approximation," From Statistics to Neural Networks: Theory and Pattern Recognition Applications, vol. 136, Springer, 1994
- Y.S. Jia, C.Y. Jia, and H.W. Qi, "A New Nu-Support Vector Machine for Training Sets with Duplicate Samples," Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, pp. 4370-4373, 2005
- C. Gold, P. Sollich, "Model selection for support vector machine classification", Neurocomputing, vol. 55(1-2), pp. 221-249, 2003 https://doi.org/10.1016/S0925-2312(03)00375-8
- S. Haykin, Neural Networks, Prentice Hall, 1999
- C. Nello, S.-H. John, An Introduction to Support Vector Machines and other kernel-based learning methods, Cambridge University Press, 2000
- V. N. Vapnik, The Nature of Statistical Learning Theory, Springer, 1995
- V.N. Vapnik, Statistical Learning Theory, John Wiley & Sons, 1998
- V.N. Vapnik, "An Overview of Statistical Learning Theory," IEEE Transactions on Neural Networks, vol. 10, no. 5, pp. 988-999, 1999 https://doi.org/10.1109/72.788640
- J. Wang, X. Wu, and C. Zhang, "Support vector machines based on K-means clustering for real-time business intelligent systems," Int. J. Business Intelligence and Data Mining, vol. 1, no. 1, pp. 54-64, 2005 https://doi.org/10.1504/IJBIDM.2005.007318
- A. Ben-Hur, D. Horn, H.T. Siegelmann, and V.N. Vapnik, "Support Vector Clustering", Journal of Machine Learning Research, vol. 2, pp. 125-137, 2001 https://doi.org/10.1162/15324430260185565
- S.-H. Jun, "Web Usage Mining Using Evolutionary Support Vector Machine", Lecture Note in Artificial Intelligence(LNAI, AI'2005), vol. 3809, pp. 1015-1020, Springer-Verlag, 2005
- L. Xuchun, Z. Yan, and E. Sung, "Sequential bootstrapped support vector machines-a SVM accelerator," Proceedings of IEEE International Joint Conference on Neural Networks, vol. 3, pp. 1437-1442, 2005 https://doi.org/10.1109/IJCNN.2005.1556086
- F. Friedrichs, C. Igel, "Evolutionary Tuning of Multiple SVM Parameters", Proceedings of the 12th European Symposium on Artificial Neural Networks, 2004
- P. Ling, Y. Wang, N. Lu, J. Y. Wang, S. Liang, C. G. Zhou, "Two-Phase Support Vector Clustering for Multi-Relational Data Mining", Proceedings of the International Conference on Cyberworlds, 2005
- Sd F. Vilarino, P. Spyridonos, J. Vitria, P. Radeva, "Experiments with SVM and Stratified Sampling with an Imbalanced Problem: Detection of Intestinal Contractions," LNCS, vol. 3687, pp. 783-792, 2005
- UCI Machine Learning Repository, http://archive.ics.uci.edu/ml/
- S.K. Thompson, Sampling, 2nd ed., John Wiley & Sons, 2002, pp. 117-127
- R. L. Scheaffer, W. Mendenhall III, R. Lyman Ott, Elementary Survey Sampling, Fifth Edition, Duxbury Press, 1996
- C.S. Ding, Q. Wu, C.T. Hsieh, and M. Pedram, "Stratified Random Sampling for Power Estimation," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 17, no. 6, pp. 465-471, 1998 https://doi.org/10.1109/43.703828
- P.A.D.I. Santos, Jr., R.J. Burke, and J.M. Tien, "Prograssive Random Sampling With Stratification," IEEE Transactions on Systems, Man, and Cybernetics-Part A:Applications and Reviews, vol. 37, no. 6, pp. 1223-1230, 2007 https://doi.org/10.1109/TSMCC.2007.905818
- M. Xing, M. Jaeger, and H. Baogang, "An Effective Stratified Sampling Scheme for Environment Maps with Median Cut Method," Proceedings of International Conference on Computer Graphics, Imaging and Visualisation, pp. 384-389, 2006
- M. Keramat, and R. Kielbasa, "A study of stratified sampling in variance reduction techniques for parametric yield estimation," Proceedings of IEEE International Symposium on Circuits and Systems, vol. 3, pp. 1652-1655, 1997
- The R Project for Statistical Computing, http://www.rproject.org
피인용 문헌
- A Cost Sensitive Part-of-Speech Tagging: Differentiating Serious Errors from Minor Errors vol.12, pp.1, 2012, https://doi.org/10.5391/IJFIS.2012.12.1.6