Communications of the ACM
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Comparing the Bayes and Typicalness Frameworks
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Transductive Confidence Machines for Pattern Recognition
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Inductive Confidence Machines for Regression
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Ridge Regression Confidence Machine
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Computationally Efficient Transductive Machines
ALT '00 Proceedings of the 11th International Conference on Algorithmic Learning Theory
Pattern Recognition and Density Estimation under the General i.i.d. Assumption
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Algorithmic Learning in a Random World
Algorithmic Learning in a Random World
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Hedging Predictions in Machine Learning
The Computer Journal
Conformal Prediction with Neural Networks
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Serum Proteomic Abnormality Predating Screen Detection of Ovarian Cancer
The Computer Journal
Transduction with confidence and credibility
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Normalized nonconformity measures for regression Conformal Prediction
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Surrogate modeling in the evolutionary optimization of catalytic materials
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Reliable probabilistic classification with neural networks
Neurocomputing
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In this paper we apply Conformal Prediction (CP) to the k-Nearest Neighbours Regression (k-NNR) algorithm and propose ways of extending the typical nonconformity measure used for regression so far. Unlike traditional regression methods which produce point predictions, Conformal Predictors output predictive regions that satisfy a given confidence level. The regions produced by any Conformal Predictor are automatically valid, however their tightness and therefore usefulness depends on the nonconformity measure used by each CP. In effect a nonconformity measure evaluates how strange a given example is compared to a set of other examples based on some traditional machine learning algorithm. We define six novel nonconformity measures based on the k-Nearest Neighbours Regression algorithm and develop the corresponding CPs following both the original (transductive) and the inductive CP approaches. A comparison of the predictive regions produced by our measures with those of the typical regression measure suggests that a major improvement in terms of predictive region tightness is achieved by the new measures.