Selected References

  1. V. Vovk, A. Gammerman, and G. Shafer, "Algorithmic Learning in a Random World", Secaucus, NJ, USA: Springer-Verlag New York, Inc., 2005.
  2. G. Shafer and V. Vovk, "A tutorial on conformal prediction", Journal of Machine Learning Research, vol. 9, pp. 371-421, 2008.
  3. V. Vovk, "On-line confidence machines are well-calibrated," in 43rd Symposium on Foundations of Computer Science. Washington, DC, USA: IEEE Computer Society, 2002, pp. 187-196.
  4. S.-S. Ho and H. Wechsler, "Query by transduction," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 30, no. 9, pp. 1557-1571, 2008.
  5. S.-S. Ho and H. Wechsler, "A Martingale Framework for Detecting Changes in Data Streams by Testing Exchangeability," IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 2113-2127, December, 2010.
  6. S.-S. Ho and H. Wechsler, "Transductive confidence machine for active learning," IJCNN, vol. 2, pp. 1435-1440 vol.2, July 2003.
  7. V. N. Balasubramanian, R. Gouripeddi, S. Panchanathan, J. Vermillion, A. Bhaskaran, and R. M. Siegel, "Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure," in Proceedings of the IEEE Conference on Computers in Cardiology, 2009.
  8. V. Balasubramanian, J. Ye, S. Chakraborty, S. Panchanathan, "Kernel Learning for Efficiency Maximization in the Conformal Predictions Framework", 9th International Conference on Machine Learning and Applications (ICMLA'10), Washington DC, Dec 2010.
  9. V. Balasubramanian, S. Chakraborty, S. Panchanathan, "Generalized Query by Transduction for Online Active Learning", IEEE ICCV 2009 International Workshop on Online Learning for Computer Vision, Kyoto, Japan, Oct 2009.
  10. G. Papadopoulos, P. Edwards, and A. Murray, "Confidence estimation methods for neural networks: a practical comparison," Neural Networks, IEEE Transactions on, vol. 12, no. 6, pp. 1287, 1278, 2002.
  11. H. Papadopoulos, "Inductive conformal prediction: Theory and application to neural networks," in Tools in Artificial Intelligence, August 2008, pp. 315-329.
  12. H. Papadopoulos, A. Gammerman, and V. Vovk, "Confidence predictions for the diagnosis of acute abdominal pain," in Artificial Intelligence Applications and Innovations III, 2009, pp. 175-184.
  13. F. Yang, H. Zhen Wang, H. Mi, C. de Lin, and W. Wen Cai, "Using random forest for reliable classification and cost-sensitive learning for medical diagnosis," BMC Bioinformatics, vol. 10, no. 1, p. S22, 2009.
  14. I. Nouretdinov, T. Melluish, and V. Vovk, "Ridge regression confidence machine," In Proceedings of the Eighteenth International Conference on Machine Learning, pp. 385-392, 2001.
  15. T. Bellotti, Z. Luo, and A. Gammerman, "Strangeness minimisation feature selection with confidence machines," in Intelligent Data Engineering and Automated Learning IDEAL 2006, 2006, pp. 978-985.
  16. D. Hardoon, Z. Hussain, and J. Shawe-Taylor, "A nonconformity approach to model selection for SVMs," University College London, Tech. Rep., 2009.
  17. Rikard Laxhammar and Goran Falkman, "Conformal prediction for distribution-independent anomaly detection in streaming vessel data", In Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques (StreamKDD '10). ACM, New York, NY, USA, 47-55, 2010.
  18. Mikhail Dashevskiy and Zhiyuan Luo, Guaranteed Network Traffic Demand Prediction Using FARIMA Models. In Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL '08), Berlin, Heidelberg, 274-281, 2008.
  19. Daniel Barbara, Carlotta Domeniconi, James P. Rogers, "Detecting outliers using transduction and statistical testing," in Proceeding of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2006), 55-64, 2006.
  20. Fayin Li and Harry Wechsler, "Open Set Face Recognition Using Transduction", IEEE Trans. Pattern Anal. Mach. Intell. Vol. 27, No. 11, 1686-1697, 2005.