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References 1. Vapnik, V. N. The Nature of Statistical Learning Theory. NY, Springer-Verlag, 1995. 2. Vapnik, V. N. Estimation of Dependencies Based on Empirical Data. Berlin, Springer-Verlag, 1982. 3. Guo, Yaxiang, Shifei Ding. Advances in Support Vector Machines. – Computer Science, Vol. 38 , 2011, No 2, pp. 14-17. 4. Wu, Q., R. Law, E. Wu. A Hybrid-Forecasting Model Reducing Gaussian Noise Based on the Gaussian Support Vector Regression Machine and Chaotic Particle Swarm Optimization. – Information Sciences, Vol. 23 , 2013, No 8, pp. 96-110. 5. Ding, G., L

, 20, 3, 1995, 273-297. [5] Franc V., Sonnenburg S., Optimized cutting plane algorithm for support vector machines, ICML 08: Proceedings of the 25th international conference on Machine learning , ACM Press 2008, 320-327. [6] Gertz E., Wright S., Object-oriented software for quadratic programming, ACM Transactions on Mathematical Software , 29, 2001, 58-81. [7] Hsieh C., Chang K., Li C.J., A comparison of methods for multi-class support vector machines, IEEE Transactions on Neural Networks , 13, 2002, 415-425. [8] Hsieh C., Chang K., Lin C.J., Keerthi S

). Support vector machine with adaptive parameters in financial time series forecasting, IEEE Transactions on Neural Networks 14 (6): 1506–1518. Chen, S. and Wang, W. (2009). Decision tree learning for freeway automatic incident detection, Expert Systems with Applications 36 (2): 4101–4105. Cristianini, N. and Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods , Cambridge University Press, New York, NY. Dong, B., Cao, C. and Lee, S.E. (2005). Applying support vector machines to predict building energy consumption

, IEEE Transactions on Neural Networks   19 (9): 1599-1614. Henson, M. A. and Seborg, D. E. (1994). Adaptive nonlinear control of a pH neutralization process, IEEE Transactions on Control System Technology   2 (3): 169-182. Huicheng, W. L. L. and Taiyi, Z. (2008). An improved algorithm on least squares support vector machines, Information Technology Journal   7 (2): 370-373. Ku, C.-C., and Lee, K. Y. (1995). Diagonal recurrent neural networks for dynamic systems control, IEEE Transactions on Neural Networks   6 (1): 144-156. Li-Juan, L., Hong-Y, S. and Jian, C

References Bala, M. and Agrawal, R.K. (2011). Optimal decision tree based multi-class support vector machine, Informatica 35(2): 197-209. 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. 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/. Bredensteiner, E.J. and Bennett, K.P. (1999

References ABBASS H. A., SARKER R. A., and NEWTON C. S., 2002: Data mining: a heuristic approach. Idea Group Publishing. ANVARI TAFTI S., 2008: Upgrading intelligent models for streamflow forecasting using distributed climatic data and snow cover. MS thesis, Faculty of Agriculture, Tarbiat Modares University, Iran. ASEFA T., KEMBLOWSKI M. W., MCKEE M., and KHALIL A., 2006: Multi-time scale stream flow prediction: The support vector machines approach. J. Hydrol., 318, 7-16. BAGHERI SHOURAKI S. and HONDA N., 1997: A new method for establishing and saving fuzzy

References 1. Megri, A. C., I. El Naqa. Prediction of the Thermal Comfort Indices Using Improved Support Vector Machine Classifiers and Nonlinear Kernel Functions. - Indoor and Built Environment, 2014, 1420326X14539693. 2. Ozer, S., C. H. Chen, H. A. Cirpan. A Set of New Chebyshev Kernel Functions for Support Vector Machine Pattern Classification. - Pattern Recognition, Vol. 44, 2011, No 7, pp. 1435-1447. 3. Yoon, C., D. Kim, W. Jung et al. AppScope: Application Energy Metering Framework for Android Smartphone Using Kernel Activity Monitoring - USENIX Annual

—Parts 1 and 2”. Mechanical Systems and Signal Processing , 40 (2), 520-544. [14] Konar, P., Chattopadhyay, P. (2011). Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs). Applied Soft Computing , 11 (6), 4203-4211. [15] Rajeswari, C., Sathiyabhama, B., Devendiran, S., Manivannan, K. (2014). Bearing fault diagnosis using wavelet packet transform, hybrid PSO and support vector machine. Procedia Engineering , 97, 1772-1783. [16] Xu, Y.J., Xiu, S.D. (2011). A new and effective method of bearing fault diagnosis using wavelet packet

References Foody, M. G., 2002. Status of Land Cover Classification Accuracy Assessment, Remote Sensing of Environment , 80, pp. 185-201. Foody, M. G., Mathur, A., 2004. A Relative Evaluation of Multiclass Image Classification by Support Vector Machines. IEEE Transactions on Geoscience and Remote Sensing , 42, pp. 1335 – 1343. Karbo, N., Schroth, R., 2009. Oblique aerial photography: a status review. Proc. 52nd Photogrammetric Week , pp. 119-125. Kamavisdar, P., Saluja, S., Agrawal, S., 2013. A Survey on Image Classification Approaches and Techniques

graded thick plates, International Journal of Advanced Structural Engineering , pp. 6-7, 2014. [32] J. Dou and J. Li, “Robust human action recognition based on spatiotemporal descriptors motion temporal templates, Optik , vol. 125, no. 7, pp. 1891-1896, 2014. [33] Q. Song, W. Hu, and X. Wenfang, “Robust support vector machine for bullet hole image classification, IEEE Transaction on Systems Man Cybernetics ,, vol. 32no. pp. 440-448, 2002. [34] S. S. Keerthi C.-J. Lin, “Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel, Neural Computation vol , 15