1 |
Cortes, C. and Vapnik, V. (1995). Support-vector networks, Machine Learning, 20, 273-297.
DOI
|
2 |
Horel, A. E. and Kennard, R. W. (1970). Ridge regression: biased estimation for nonorthogonal problems, Technometrics, 12, 55-67.
DOI
|
3 |
Douglas, R. (2015). Gaussian mixture models, Encyclopedia of biometrics.
|
4 |
Kim, J. I. (2013). The determinants of subway riderships at AM-peak in Daegu metropolitan city: focusing on the land use of station neighborhood areas, Journal of Transport Research, 20, 15-25.
DOI
|
5 |
Kim, J. S. (2016). Subway congestion prediction and recommendation system using big data analysis, Journal of Digital Convergence, 14, 289-295.
DOI
|
6 |
Lee, J., Go, J. Y., Jeon, S., and Jun, C. (2015). A study of land use characteristics by types of subway station areas in Seoul analyzing patterns of transit ridership, The Korea Spatial Planning Review, 84, 35-53.
DOI
|
7 |
R Development Core Team (2010). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, ISBN 3-900051-07-0. http://www.R-project.org.
|
8 |
Ridgeway, G. (2007). Generalized Boosted Models: A guide to the gbm package, https://cran.r-project.org/web/packages/gbm
|
9 |
Shon, E. Y., Kwon, B. W., and Lee, M. H. (2004). Modelling the subway demand estimation by station using the multiple regression analysis by category, Journal of Korea Society of Transportation, 22, 33-42.
|
10 |
Song, J. (1991). A study on prediction of passenger demand in Seoul Subway, Statistical Consulting, 6.
|
11 |
Tianqi, C. and Carlos, G. (2016). XGBoost: A Scalable Tree Boosting System, KDD '16 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794.
|
12 |
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society B, 58, 267-288.
|
13 |
Breiman, L., Friedman. J., Olshen, R., and Stone, C. (1984). Classification and Regression Trees, Chapman and Hall, New York.
|
14 |
Breiman, L. (2001). Random forests, Machine Learning, 45, 5-32.
DOI
|