Optimal Cross-Validation Split Ratio: Experimental Investigation
作者： Cyril Goutte , Jan Larsen
DOI: 10.1007/978-1-4471-1599-1_104
关键词: Metric (mathematics) 、 Cross-validation 、 Sensitivity (control systems) 、 Applied mathematics 、 Process (computing) 、 Set (abstract data type) 、 Estimator 、 Mathematics 、 Kernel (statistics) 、 Statistics
摘要: Cross-validation is a widespread method for assessing the generalisation ability of model in order to tune regularisation parameter or other hyper-parameters learning process. The use cross-validation requires set yet an additional parameter, split ratio. Few texts have investigated theoretically asymptotic setting this ratio, and no consensus has emerged. In contribution, we investigate sensitivity optimal ratio on particular model, non-parametric kernel estimator with adaptive metric.
springer.com
core.ac.uk
sci-hub.st 参考文章(13)
C. Goutte, J. Larsen, Adaptive metric kernel regression Neural Networks for Signal Processing VIII. Proceedings of the 1998 IEEE Signal Processing Society Workshop (Cat. No.98TH8378). pp. 184- 193 ,(1998) , 10.1109/NNSP.1998.710648
Geoffrey Hinton, Carl Edward Rasmussen, Evaluation of gaussian processes and other methods for non-linear regression University of Toronto. ,(1997)
Jun Shao, Linear Model Selection by Cross-validation Journal of the American Statistical Association. ,vol. 88, pp. 486- 494 ,(1993) , 10.1080/01621459.1993.10476299
Michael Kearns, A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with Consequences for the Training-Test Split neural information processing systems. ,vol. 9, pp. 183- 189 ,(1995) , 10.1162/NECO.1997.9.5.1143
Wolfgang Hrdle, Applied Nonparametric Regression ,(1990)
Hirotugu Akaike, Fitting autoregressive models for prediction Annals of the Institute of Statistical Mathematics. ,vol. 21, pp. 243- 247 ,(1969) , 10.1007/978-1-4612-1694-0_10
Jeng-Neng Hwang, Shyh-Rong Lay, M. Maechler, R.D. Martin, J. Schimert, Regression modeling in back-propagation and projection pursuit learning IEEE Transactions on Neural Networks. ,vol. 5, pp. 342- 353 ,(1994) , 10.1109/72.286906
Jerome H. Friedman, Multivariate Adaptive Regression Splines Annals of Statistics. ,vol. 19, pp. 1- 141 ,(1991) , 10.1214/AOS/1176347963
M. Stone, Cross-Validatory Choice and Assessment of Statistical Predictions Journal of the Royal Statistical Society: Series B (Methodological). ,vol. 36, pp. 111- 133 ,(1974) , 10.1111/J.2517-6161.1974.TB00994.X
Yann LeCun, John Denker, Sara Solla, None, Optimal Brain Damage neural information processing systems. ,vol. 2, pp. 598- 605 ,(1989)