The ‘K’ in K-fold cross validation

ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Volumn , Issue , 2012, Pages 441-446

  Anguita, Davide ^a   Ghelardoni, Luca ^a   Ghio, Alessandro ^a   Oneto, Luca ^a   Ridella, Sandro ^a  

^a UNIVERSITY OF GENOA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; ITERATIVE METHODS; NEURAL NETWORKS;

CARDINALITIES; K FOLD CROSS VALIDATIONS; MODEL SELECTION; PROBABILITY OF MISCLASSIFICATION; RULE OF THUMB;

LEARNING SYSTEMS;

EID: 84947748341     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. V.N. Vapnik. Statistical learning theory. Wiley-Interscience, 1998.
2. O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee. Choosing multiple parameters for support vector machines. Machine Learning, 46(1):131–159, 2002.
3. I. Guyon, A. Saffari, G. Dror, and G. Cawley. Model selection: beyond the bayesian– frequentist divide. The Journal of Machine Learning Research, 11:61–87, 2010.
4. D. Anguita, Ghio A., S. Ridella, and D. Sterpi. K-fold cross validation for error rate estimate in support vector machines. In Proc. of the Int. Conf. on Data Mining, 2009.
5. T.G. Dietterich. Approximate statistical tests for comparing supervised classification learning algorithms. Neural computation, 10(7):1895–1923, 1998.
6. C.W. Hsu, C.C. Chang, and C.J. Lin. A Practical Guide to Support Vector Classification. Technical report, 2000.
7. T. Hastie, R. Tibshirani, J. Friedman, and J. Franklin. The elements of statistical learning: data mining, inference and prediction. The Mathematical Intelligencer, 27(2):83–85, 2005.
8. L. Roberge, S. B. Long, and D. B. Burnham. Data warehouses and data mining tools for the legal profession: using information technology to raise the standard of practice. Syracuse Law Review, 2002.
9. B. L. Milenova, J. S. Yarmus, and M. M. Campos. Svm in oracle database 10g: Removing the barriers to widespread adoption of support vector machines. In Proc. of the 31st Int. Conf. on Very Large Data Bases, pages 1152–1163, 2005.
10. C.J. Clopper and E.S. Pearson. The use of confidence intervals for fiducial limits illustrated in the case of the binomial. Biometrika, 1934.
11. D. Anguita, L. Ghelardoni, A. Ghio, and S. Ridella. A survey of old and new results for the test error estimation of a classifier. Journal of Artificial Intelligence and Soft Computing Research (in press).
12. R. Kohavi. A study of cross-validation and bootstrap for accuracy estimation and model selection. In International joint Conference on artificial intelligence, volume 14, pages 1137–1145, 1995.
13. S. Arlot and A. Celisse. A survey of cross-validation procedures for model selection. Statistics Surveys, 4:40–79, 2010.
14. G. Rätsch, T. Onoda, and K.R. Müller. Soft margins for adaboost. Machine Learning, 42(3):287–320, 2001.