EVALUATION OF THE IMPACT OF THE PRE-PROCESSING OF DATA ON THE EFFECTIVENESS AND ACCURACY OF SVM

Singh, V.P., Woolhiser, D.A. Mathematical Modeling of Watershed Hydrology, Journal of Hydrology Engineering, vol: 7, issue: 4, pp 269–343, 2002.

Pyle, D., Data preparation for data mining (Vol. 1). Morgan Kaufmann, 1999.

Shawe-Taylor, J., Cristianini, N. An introduction support vector machines and other kernel-based learning methods, Cambridge University Press, 2000.

Chen, S.-T. et al. Statistical downscaling of daily precipitation using support vector machines and multivariate analysis, Journal of Hydrology, 385(1), pp 13-22, 2010.

Ceperic, V. et al. Sparse multikernel support vector regression machines trained by active learning, Expert Systems with Applications 39, pp 11029–11035, 2012.

Cortes, C., Vapnik N. V. Support-vector networks. Machine Learning, 20 (3), pp 273–297, 1995.

John, G., Langley, P., Static Versus Dynamic Sampling for Data Mining, n Proceedings of the Second International Conference on Knowledge Discovery and Data Mining,1996.

Dash, M., Liu, H., Feature Selection for Classification, Intelligent Data Analysis 1, pp 131-156, 1997.

Vapnik, V., The Nature of Statistical Learning Theory, 2nd ed., Springer Evaluation of Sampling for Data Mining of Association Rules, 1999.

Smola, A. J., Schölkopf, B., A tutorial on support vector regression. Statistics and computing, 14(3), pp 199-222, 2004. GeoConference on Water Resources. Forest, Marine and Ocean Ecosystems

Singh, V.P., Woolhiser, D.A. Mathematical Modeling of Watershed Hydrology, Journal of Hydrology Engineering, vol: 7, issue: 4, pp 269–343, 2002.

Pyle, D., Data preparation for data mining (Vol. 1). Morgan Kaufmann, 1999.

Shawe-Taylor, J., Cristianini, N. An introduction support vector machines and other kernel-based learning methods, Cambridge University Press, 2000.

Chen, S.-T. et al. Statistical downscaling of daily precipitation using support vector machines and multivariate analysis, Journal of Hydrology, 385(1), pp 13-22, 2010.

Ceperic, V. et al. Sparse multikernel support vector regression machines trained by active learning, Expert Systems with Applications 39, pp 11029–11035, 2012.

Cortes, C., Vapnik N. V. Support-vector networks. Machine Learning, 20 (3), pp 273–297, 1995.

John, G., Langley, P., Static Versus Dynamic Sampling for Data Mining, n Proceedings of the Second International Conference on Knowledge Discovery and Data Mining,1996.

Dash, M., Liu, H., Feature Selection for Classification, Intelligent Data Analysis 1, pp 131-156, 1997.

Vapnik, V., The Nature of Statistical Learning Theory, 2nd ed., Springer Evaluation of Sampling for Data Mining of Association Rules, 1999.

Smola, A. J., Schölkopf, B., A tutorial on support vector regression. Statistics and computing, 14(3), pp 199-222, 2004. GeoConference on Water Resources. Forest, Marine and Ocean Ecosystems