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Abstract: Instance-based learning (IBL) algorithms have proved to be successful in many applications. However, as opposed to standard statistical methods, a prediction in IBL is usually given without characterizing its confidence. In this paper, we propose an IBL method that allows for deriving set-valued predictions that cover the correct answer (label) with high probability. Our method makes use of a formal model of the heuristic inference principle suggesting that similar instances do have similar... (Update)
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BibTeX entry: (Update)
@misc{ llermeier-instancebased,
author = "Eyke Hüllermeier",
title = "Instance-Based Prediction with Guaranteed Confidence",
url = "citeseer.ist.psu.edu/691812.html" }
Citations (may not include all citations):947 Statistical Learning Theory (context) - Vapnik - 1998
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5 A review and empirical comparison of feature weighting metho.. (context) - Wettschereck, Aha et al. - 1997
2 Transductive confidence machines for pattern recognition - Proedrou, Nouretdinov et al. - 2002
2 Kluwer Academic Publ (context) - Learning, Aha - 1997
1 Instance-based learning of credible label sets (context) - Hullermeier - 2003
1 Focusing search by using problem solving experience (context) - Hullermeier - 2000
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