Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Algorithmic Learning in a Random World
Algorithmic Learning in a Random World
Hedging Predictions in Machine Learning
The Computer Journal
Learning to Predict One or More Ranks in Ordinal Regression Tasks
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Cancer informatics by prototype networks in mass spectrometry
Artificial Intelligence in Medicine
Hedged predictions for traditional Chinese chronic gastritis diagnosis with confidence machine
Computers in Biology and Medicine
Efficient AdaBoost Region Classification
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Meta-conformity approach to reliable classification
Intelligent Data Analysis
Learning Nondeterministic Classifiers
The Journal of Machine Learning Research
Conformal prediction for distribution-independent anomaly detection in streaming vessel data
Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques
Single-stacking conformity approach to reliable classification
AIMSA'10 Proceedings of the 14th international conference on Artificial intelligence: methodology, systems, and applications
k-version-space multi-class classification based on k-consistency tests
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Incremental threshold learning for classifier selection
Neurocomputing
Expert Systems with Applications: An International Journal
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Conformal prediction uses past experience to determine precise levels of confidence in new predictions. Given an error probability ε, together with a method that makes a prediction ŷ of a label y, it produces a set of labels, typically containing ŷ, that also contains y with probability 1 ε. Conformal prediction can be applied to any method for producing ŷ: a nearest-neighbor method, a support-vector machine, ridge regression, etc. Conformal prediction is designed for an on-line setting in which labels are predicted successively, each one being revealed before the next is predicted. The most novel and valuable feature of conformal prediction is that if the successive examples are sampled independently from the same distribution, then the successive predictions will be right 1 ε of the time, even though they are based on an accumulating data set rather than on independent data sets. In addition to the model under which successive examples are sampled independently, other on-line compression models can also use conformal prediction. The widely used Gaussian linear model is one of these. This tutorial presents a self-contained account of the theory of conformal prediction and works through several numerical examples. A more comprehensive treatment of the topic is provided in Algorithmic Learning in a Random World, by Vladimir Vovk, Alex Gammerman, and Glenn Shafer (Springer, 2005).