Cross-conformal predictors

Annals of Mathematics and Artificial Intelligence

Volumn 74, Issue 1-2, 2015, Pages 9-28

  Vovk, Vladimir ^a  

^a UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

Conformal predictors; Cross validation; Inductive conformal predictors; Tolerance regions

Indexed keywords

EID: 84930413676     PISSN: 10122443     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10472-013-9368-4     Document Type: Article

References (21)

1. Balasubramanian, V.N.: Conformal predictions in multimedia pattern recognition. Ph.D. thesis, Arizona State University (2010)
2. Balasubramanian, V.N., Chakraborty, S., Panchanathan, S.: Conformal predictions for information fusion: a comparative study of p-value combination methods. Ann. Math. Artif. Intell. (2013, this issue)
3. Breiman, L., Spector, P.: Submodel selection and evaluation in regression: the X-random case. Int. Stat. Rev. 60, 291–319 (1992)
4. Efron, B.: Bootstrap methods: another look at the jackknife. Ann. Stat. 7, 1–26 (1979)
5. Efron, B.: Estimating the error rate of a prediction rule: some improvements on cross-validation. J. Am. Stat. Assoc. 78, 316–331 (1983)
6. Fisher, R.A.: Combining independent tests of significance. Am. Stat. 2, 30 (1948)
7. Fraser, D.A.S.: Nonparametric Methods in Statistics. Wiley, New York (1957)
8. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 119–139 (1997)
9. Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Data Anal. 29, 1189–1232 (2001)
10. Friedman, J.H.: Stochastic gradient boosting. Comput. Stat. Data Anal. 38, 367–378 (2002)
11. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn. Springer, New York (2009)
12. Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 1137–1143. Morgan Kaufmann, San Mateo, CA (1995)
13. Lei, J., Rinaldo, A., Wasserman, L.: A conformal prediction approach to explore functional data. Ann. Math. Artif. Intell. (2013). doi:10.1007/s10472-013-9366-6
14. Lei, J., Wasserman, L.: Distribution free prediction bands. Tech. Rep. arXiv: 1203.5422 [stat.ME]. e-Print archive (2012, to appear in JRRSB)
15. Mosteller, F., Tukey, J.W.: Data analysis, including statistics. In: Lindzey, G., Aronson, E. (eds.) Handbook of Social Psychology, 2nd edn., vol. 2, pp. 80–203. Addison-Wesley, Reading, MA (1968)
16. Papadopoulos, H., Vovk, V., Gammerman, A.: Qualified predictions for large data sets in the case of pattern recognition. In: Proceedings of the First International Conference on Machine Learning and Applications (ICMLA), pp. 159–163. CSREA Press, Las Vegas, NV (2002)
17. Simard, P., LeCun, Y., Denker, J.: Efficient pattern recognition using a new transformation distance. In: Hanson, S., Cowan, J., Giles, C. (eds.) Advances in Neural Information Processing Systems, vol. 5, pp. 50–58. Morgan Kaufmann, San Mateo, CA (1993)
18. Stone, M.: Cross-validatory choice and assessment of statistical predictions (with discussion). J. R. Stat. Soc. Ser. B 36, 111–147 (1974)
19. Vanderlooy, S., van der Maaten, L., Sprinkhuizen-Kuyper, I.: Off-line learning with transductive confidence machines: an empirical evaluation. In: Perner, P. (ed.) Proceedings of the Fifth International Conference on Machine Learning and Data Mining in Pattern Recognition. Lecture Notes in Artificial Intelligence, vol. 4571, pp. 310–323. Springer, Berlin (2007)
20. Vovk, V.: Conditional validity of inductive conformal predictors. In: Hoi, S.C.H., Buntine, W. (eds.) JMLR: Workshop and Conference Proceedings, vol. 25 (Asian Conference on Machine Learning), pp. 475–490 (2012). The journal version to appear in Machine Learning (ACML 2012 Special Issue)
21. Vovk, V., Gammerman, A., Shafer, G.: Algorithmic Learning in a Random World. Springer, New York (2005)