Off-Line Learning with Transductive Confidence Machines: An Empirical Evaluation


The recently introduced transductive confidence machines (TCMs) framework allows to extend classifiers such that they satisfy the calibration property. This means that the error rate can be set by the user prior to classification. An analytical proof of the calibration property was given for TCMs applied in the on-line learning setting. However, the nature… (More)
DOI: 10.1007/978-3-540-73499-4_24


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