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Transductive confidence machines

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Abstract The field of statistical learning theory has developed alternatives to induction. Instead of using all the available points to induce a model, the data, or usually a small subset of the data, can be used to estimate unknown properties of points to be tested (e.g., membership to a class). This idea leads to algorithms that use standard statistical tests to compute the confidence on the estimation. Using transduction, researchers have built transductive confidence machines which are able to estimate the unknown class of a point and attach confidence to the estimate, and also to determine outliers in a data set. WIREs Comp Stat 2011 3 216–220 DOI: 10.1002/wics.154 This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Pattern Recognition Statistical Learning and Exploratory Methods of the Data Sciences > Knowledge Discovery

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Statistical Learning and Exploratory Methods of the Data Sciences > Knowledge Discovery
Statistical Learning and Exploratory Methods of the Data Sciences > Pattern Recognition

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