Page 143 - Proceeding of Atrans Young Researcher's Forum 2019
P. 143

“Transportation for A Better Life:
                                                                                                                       Smart Mobility for Now and Then”

                                                                                    23 August 2019, Bangkok, Thailand

             [55] E. Alpaydin, Introduction to Machine Learning.
             TBS, 2009.
             [56] Y. Bazi and F. Melgani, “Toward an Optimal
             SVM  Classification  System  for  Hyperspectral
             Remote  Sensing  Images,”  IEEE  Trans.  Geosci.
             REMOTE Sens., vol. 44, 2006.
             [57]  G.  M.  Foody,  “Local  characterization  of
             thematic  classification  accuracy  through  spatially
             constrained  confusion  matrices,”  Int.  J.  Remote
             Sens., vol. 26, no. 6, pp. 1217–1228, Mar. 2005.
             [58] A. M. Hay, “The derivation of global estimates
             from a confusion matrix,” Int. J. Remote Sens., vol.
             9, no. 8, pp. 1395–1398, Aug. 1988.
             [59] T. Chai and R. R. Draxler, “Root mean square
             error  (RMSE)  or  mean  absolute  error  (MAE)?,”
             Geosci. Model Dev. Discuss., vol. 7, no. 1, pp. 1525–
             1534, Feb. 2014.
             [60] C. Willmott and K. Matsuura, “Advantages of
             the mean absolute error (MAE) over the root mean
             square  error  (RMSE)  in  assessing  average  model
             performance,” Clim. Res., vol. 30, pp. 79–82, 2005.
             [61] D. D. Patil, V. M. Wadhai, and J. A. Gokhale,
             “Evaluation  of  Decision  Tree  Pruning  Algorithms
             for Complexity and Classification Accuracy,” Int. J.
             Comput. Appl., vol. 11, no. 2, pp. 23–30, Dec. 2010.
             [62] T. Chai and R. R. Draxler, “Root mean square
             error  (RMSE)  or  mean  absolute  error  (MAE)?  –
             Arguments  against  avoiding  RMSE  in  the
             literature,”  Geosci.  Model  Dev.,  vol.  7,  no.  3,  pp.
             1247–1250, Jun. 2014.
             [63] R. Y. Rubinstein and D. P. Kroese, Simulation
             and the Monte Carlo Method, 3 edition. Hoboken,
             New Jersey: Wiley, 2016.
             [64] D. V. Dao, H.-B. Ly, S. H. Trinh, T.-T. Le, and
             B. T. Pham, “Artificial Intelligence Approaches for
             Prediction of Compressive Strength of Geopolymer
             Concrete,” Materials, vol. 12, no. 6, p. 983, 2019.
             [65]  T.  T.  Le,  J.  Guilleminot,  and  C.  Soize,
             “Stochastic  continuum  modeling  of  random
             interphases from atomistic simulations. Application
             to  a  polymer  nanocomposite,”  Comput.  Methods
             Appl. Mech. Eng., vol. 303, pp. 430–449, 2016.
             [66]  C.  Soize,  Uncertainty  Quantification:  An
             Accelerated Course with Advanced Applications in
             Computational Engineering. Springer International
             Publishing, 2017.












                                                           122
   138   139   140   141   142   143   144   145   146   147   148