234
Gene, H., Loan, V., Charles, F., 1996. Matrix Computations. 3rd ed. London: The Johns Hopkins University Press.
Ghosh, R., Schunnesson, H., Kumar, U., 2015. The use of specific energy in rotary drilling: The effect of
operational parameters. s.l., International Symposium on the Application of Computers and Operations Research in the Mineral Industry.
Gretton, A., Bousquet, O., Smola, A.J., Scholkopf, B., 2005. Measuring statistical dependence with
Hilbert-Schmidt norms. s.l., Springer-Verlag, pp. 63–77.
Kadkhodaie-Ilkhchi, A., Monteiro, S.T., Ramos, F., Hatherly, P., 2010. Rock Recognition From MWD
Data: A Comparative Study of Boosting, Neural Networks, and Fuzzy Logic. IEEE GEOSCIENCE
AND REMOTE SENSING LETTERS, Volume 7, pp. 680–684.
Kohavi, R., San Mateo, Kaufmann, M., 1995. A study of cross-validation and bootstrap for accuracy
estimation and model selection. s.l., s.n., p. 1137–1143.
Kononenko, I., Šimec Marko, E., Šikonja, R., 1997. Overcoming the myopia of inductive learning algorithms with RELIEFF. Applied Intelligence, pp. 39–55.
Laubscher, D.H., Jakubec, J., 2001. The MRMR rock mass classification for jointed rock masses.
Underground Mining Methods: Engineering Fundamentals and International Case Studies. W.A.
Hustrulid and R.L. Bullock (eds) Society of Mining Metallurgy and Exploration, pp. 475–481.
Liaghat, S., Mansoori, E.G., 2016. Unsupervised selection of informative genes in microarray gene
expression data. International Journal of Applied Pattern Recognition, pp. 351–367.
Liaghat, S., Mansoori, E.G., 2018. Filter-based unsupervised feature selection using Hilbert–Schmidt
independence criterion. International Journal of Machine Learning and Cybernetics, pp. 1–16.
Nadaraya, E.A., 1964. On Estimating Regression. Theory of Probability and its Applications.
Neff, J.M., 2003. Biological effects of drilling fluids, drill cuttings and produced waters. In Long-term
environmental effects of offshore oil and gas development, pp. 479–548.
Shawe-Taylor, J., Cristianini, N., 2004. Kernel Methods for Pattern Analysis. s.l.: Cambridge University
Press.
Song, L., Smola, A., Gretton, A., Bedo, J., Borgwardt, K., 2012. Feature selection via dependence maximization. J. Machine Learning Research, Volume 13, pp. 1393–1434.
Starr, R.C., Ingleton, R.A., 1992. A new method for collecting core samples without a drilling rig.
Groundwater Monitoring & Remediation, pp. 91–95.
Van Eldert, J., Schunnesson, H., Johansson, D. & Saiang, D., 2018. Measurement While Drilling
(MWD) Technology for Blasting Damage Calculation. s.l., 12th International Symposium on Rock
Fragmentation by Blasting.
Wellmer, F.W., Dalheimer, M., Wagner, M., 2010. Economic Evaluations in Exploration. s.l.: Springer.
Gene, H., Loan, V., Charles, F., 1996. Matrix Computations. 3rd ed. London: The Johns Hopkins University Press.
Ghosh, R., Schunnesson, H., Kumar, U., 2015. The use of specific energy in rotary drilling: The effect of
operational parameters. s.l., International Symposium on the Application of Computers and Operations Research in the Mineral Industry.
Gretton, A., Bousquet, O., Smola, A.J., Scholkopf, B., 2005. Measuring statistical dependence with
Hilbert-Schmidt norms. s.l., Springer-Verlag, pp. 63–77.
Kadkhodaie-Ilkhchi, A., Monteiro, S.T., Ramos, F., Hatherly, P., 2010. Rock Recognition From MWD
Data: A Comparative Study of Boosting, Neural Networks, and Fuzzy Logic. IEEE GEOSCIENCE
AND REMOTE SENSING LETTERS, Volume 7, pp. 680–684.
Kohavi, R., San Mateo, Kaufmann, M., 1995. A study of cross-validation and bootstrap for accuracy
estimation and model selection. s.l., s.n., p. 1137–1143.
Kononenko, I., Šimec Marko, E., Šikonja, R., 1997. Overcoming the myopia of inductive learning algorithms with RELIEFF. Applied Intelligence, pp. 39–55.
Laubscher, D.H., Jakubec, J., 2001. The MRMR rock mass classification for jointed rock masses.
Underground Mining Methods: Engineering Fundamentals and International Case Studies. W.A.
Hustrulid and R.L. Bullock (eds) Society of Mining Metallurgy and Exploration, pp. 475–481.
Liaghat, S., Mansoori, E.G., 2016. Unsupervised selection of informative genes in microarray gene
expression data. International Journal of Applied Pattern Recognition, pp. 351–367.
Liaghat, S., Mansoori, E.G., 2018. Filter-based unsupervised feature selection using Hilbert–Schmidt
independence criterion. International Journal of Machine Learning and Cybernetics, pp. 1–16.
Nadaraya, E.A., 1964. On Estimating Regression. Theory of Probability and its Applications.
Neff, J.M., 2003. Biological effects of drilling fluids, drill cuttings and produced waters. In Long-term
environmental effects of offshore oil and gas development, pp. 479–548.
Shawe-Taylor, J., Cristianini, N., 2004. Kernel Methods for Pattern Analysis. s.l.: Cambridge University
Press.
Song, L., Smola, A., Gretton, A., Bedo, J., Borgwardt, K., 2012. Feature selection via dependence maximization. J. Machine Learning Research, Volume 13, pp. 1393–1434.
Starr, R.C., Ingleton, R.A., 1992. A new method for collecting core samples without a drilling rig.
Groundwater Monitoring & Remediation, pp. 91–95.
Van Eldert, J., Schunnesson, H., Johansson, D. & Saiang, D., 2018. Measurement While Drilling
(MWD) Technology for Blasting Damage Calculation. s.l., 12th International Symposium on Rock
Fragmentation by Blasting.
Wellmer, F.W., Dalheimer, M., Wagner, M., 2010. Economic Evaluations in Exploration. s.l.: Springer.