Machine Learning
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Highlighting Hard Patterns via AdaBoost Weights Evolution
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Algorithmic Learning in a Random World
Algorithmic Learning in a Random World
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A Tutorial on Conformal Prediction
The Journal of Machine Learning Research
Off-Line Learning with Transductive Confidence Machines: An Empirical Evaluation
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Meta-Typicalness Approach to Reliable Classification
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
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The task of region classification is to construct class regions containing the correct classes of the objects being classified with an error probability *** *** [0,1]. To turn a point classifier into a region classifier, the conformal framework is employed [11,14]. However, to apply the framework we need to design a non-conformity function. This function has to estimate the instance's non-conformity for the point classifier used. This paper introduces a new non-conformity function for AdaBoost. The function has two main advantages over the only existing non-conformity function for AdaBoost. First, it reduces the time complexity of computing class regions with a factor equal to the size of the training data. Second, it results in statistically better class regions.