1 |
Yau, N. J., Yang, J. B., & Hsieh, T. Y. (1999). Inducing rules for selecting retaining wall systems, Construction Management and Economics, 17(1), pp. 91-98.
DOI
|
2 |
Kim, J. Y., Park, U. Y., Kim, G. H., & Kim, J. K. (2004). A Study on the Selection Model of Retaining Wall Methods Using Case-Based Reasoning, Journal of Korea Institute of Construction Engineering and Management, 5(6), pp. 76-83.
|
3 |
Friedman, J., & Meulman, J. (2003). Multiple additive regression trees with application in epidemiology, Statistics in Medicine, 22(9), pp. 1365-1381.
DOI
|
4 |
Godinho, S., Guiomar, N., & Gil, A. (2018). Estimating tree canopy cover percentage in a mediterranean silvopastoral systems using Sentinel-2A imagery and the stochastic gradient boosting algorithm, International Journal of Remote Sensing, 39(14), pp 4640-4662.
DOI
|
5 |
Kim, J. Y., Park, U. Y., & Kang, K. I. (2002). A study on the selection system of retaining wall methods using neural network, Journal of the Architectural Institute of Korea, 18(10), pp. 69-76.
|
6 |
Kim, J. Y., Park U. Y., & Kim, G. H. (2006). A Study on the Selection of Retaining Wall Methods Using Neural Networks and Cased-based Reasoning, Journal of Architectural Institute of Korea, 22(5), pp. 187-194.
|
7 |
Li, L., Wu, Y., & Ye, M. (2014). Multi-class Image Classification Based on Fast Stochastic Gradient Boosting, Informatica, 38(3), pp. 145-153.
|
8 |
Ogutu, J. O., Piepho, H. P., & Schulz-Streeck, T. (2011). A comparison of random forests, boosting and support vector machines for genomic selection, BMC Proceedings, 5(S3) p. 11.
|
9 |
Bahk, E. (2019). Enormous underground transit terminal to be built in Gangnam, The Korea Times, 2019 06 12 - accessed 2020 12 15, http://www.koreatimes.co.kr/www/nation/2019/06/113_270487.html
|
10 |
Cheng, M. Y., Peng, H. S., Wu, Y. W., & Chen T. L. (2010). Estimate at completion for construction projects using evolutionary support vector machine inference model, Automation in Construction, 19(5), pp 619-629.
DOI
|
11 |
Choi, M., & Lee, G. (2010). Decision tree for selecting retaining wall systems based on logistic regression analysis, Automation in Construction, 19(7), pp. 917-928.
DOI
|
12 |
Park, U. Y., & Kim, J. Y. (2006). A study on the selection model of retaining wall methods using support vector machines, Korea Journal of Construction Engineering and Management, 7(2), pp. 118-126.
|
13 |
Shin, Y. (2019). Application of stochastic gradient boosting approach to early prediction of safety accidents at construction site, Advances in Civil Engineering, 2019, pp. 1-9.
DOI
|
14 |
Yang, J. B. (2002). A rule induction-based knowledge system for retaining wall selection, Expert Systems with Application, 23(3), pp. 273-279.
DOI
|
15 |
Yau, N. J., & Yang, J. B. (1998). Applying case-based reasoning technique to retaining wall selection, Automation in Construction, 7(4), pp. 271-283.
DOI
|
16 |
Xu, Q., Xiong, Y., Dai, H., Kumari, K. M., Xu, Q., Ou, H. Y., & Wey, D. Q. (2017). PDC-SGB: Prediction of effective drug combinations using a stochastic gradient boosting algorithm, Journal of Theoretical Biology, 417, pp. 1-7.
DOI
|
17 |
Freund, Y., & Schapire, R. E. (1996). Experiments with a new boosting algorithm, in Machine Learning: Proceedings of the Thirteenth International Conference, 13, pp. 148-156.
|
18 |
Ding, H., Li, G., Dong, X., & Lin, Y. (2018). Prediction of Pillar Stability for Underground Mines using the Stochastic Gradient Boosting Technique, IEEE Access, 6, pp. 69253-69264.
DOI
|
19 |
Elish, M. (2018). Enhance prediction of vulnerable web components using Stochastic Gradient Boosting Trees, International Journal of Web Information System, 15(2), pp. 201-214.
DOI
|
20 |
Feng, K. Y., Cai, Y. D., & Chou, K. C. (2005). Boosting classifier for predictinig protein domain structural class, Biochemical and Biophysical Research Communications, 334, pp. 213-217.
DOI
|
21 |
Halteh, K., Kumar, K., & Gepp, A. (2018). Using Cutting-edge tree-based stochastic models to predict credit risk, Risks, 6(2), pp. 55.
DOI
|
22 |
Sheu, H. B. (1996). Application of expert systems and neural networks for retaining wall system selection, Master's Thesis, Nat. Central Univ., Taiwan, R.O.C.
|
23 |
Tixier, A. J. P., Hallowell, M. R., Rajagopalan, B., & Bowman, D. (2016). Application of machine learning to construction injury prediction, Automation in Construction, 69, pp. 102-114.
DOI
|
24 |
Yang, Y., Yin, J. H., Yuan, J. X., & Schulyer, J. N. (2003). An expert system for selection of retaining walls and groundwater controls in deep excavation, Computers and Geotechnics, 30(8), pp. 707-719.
DOI
|
25 |
Zhou, J., Li, X. B., & Mitri, H. S. (2015). Comparative performance of six supervised learning methods for the development of models of pillar stability, Nature Hazards, 79(1), pp. 291-316.
DOI
|
26 |
Friedman, J. H. (2002). Stochastic Gradient Boositng, Computational Statistics and Data Analysis, 38(4), pp 367-378.
DOI
|
27 |
An, S. H., Park, U. Y., Kang, K. I., M. Cho Y., & Cho, H. H. (2007). Application of support vector machines in assessing conceptual cost estimates, J. Comput. Civil Eng, 21(4), pp 259-264.
DOI
|
28 |
Chirici, G., Scotti, R., Montaghi, A., Barbati, A., Cartisano, R., Lopez, G., Marchetti, M., R. McRoberts, - E., Olsson, H., & Corona, P. (2013). Stochastic gradient boosting classification trees for forest fuel types mapping through airborne laser scanning and IRS LISS-III imagery, International Journal of Applied Earth Observation and Geoinformation, 25, pp. 87-97.
DOI
|
29 |
Kumar, P. R. & Ravi, V. (2007). Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review, European Journal of Operational Research, 180(1), pp. 1-28.
DOI
|