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