Cross-Conformal Prediction with Ridge Regression

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• 作者：Harris Papadopoulos (7)
  
  7. Computer Science and Engineering Department ; Frederick University ; 7 Y. Frederickou St. ; Palouriotisa ; 1036 ; Nicosia ; Cyprus
  
• 关键词：Conformal prediction ; Cross ; validation ; Inductive conformal prediction ; Prediction regions ; Tolerance regions
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9047
• 期：1
• 页码：260-270
• 全文大小：203 KB
• 参考文献：1. Bache, K., Lichman, M.: UCI machine learning repository (2013). http://archive.ics.uci.edu/ml
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  6. Papadopoulos, H, Haralambous, H (2011) Reliable prediction intervals with regression neural networks. Neural Networks 24: pp. 842-851 CrossRef
  7. Papadopoulos, H, Proedrou, K, Vovk, V, Gammerman, A Inductive confidence machines for regression. In: Elomaa, T, Mannila, H, Toivonen, H eds. (2002) Machine Learning: ECML 2002. Springer, Heidelberg, pp. 345-356 CrossRef
  8. Papadopoulos, H, Vovk, V, Gammerman, A (2011) Regression conformal prediction with nearest neighbours. Journal of Artificial Intelligence Research 40: pp. 815-840
  9. Proedrou, K, Nouretdinov, I, Vovk, V, Gammerman, A Transductive confidence machines for pattern recognition. In: Elomaa, T, Mannila, H, Toivonen, H eds. (2002) Machine Learning: ECML 2002. Springer, Heidelberg, pp. 381-390 CrossRef
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  12. Vovk, V.: Cross-conformal predictors. Annals of Mathematics and Artificial Intelligence (2013). http://dx.doi.org/10.1007/s10472-013-9368-4
  13. Vovk, V, Gammerman, A, Shafer, G (2005) Algorithmic Learning in a Random World. Springer, New York
  
• 作者单位：Statistical Learning and Data Sciences
• 丛书名：978-3-319-17090-9
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Cross-Conformal Prediction (CCP) is a recently proposed approach for overcoming the computational inefficiency problem of Conformal Prediction (CP) without sacrificing as much informational efficiency as Inductive Conformal Prediction (ICP). In effect CCP is a hybrid approach combining the ideas of cross-validation and ICP. In the case of classification the predictions of CCP have been shown to be empirically valid and more informationally efficient than those of the ICP. This paper introduces CCP in the regression setting and examines its empirical validity and informational efficiency compared to that of the original CP and ICP when combined with Ridge Regression.