Abstract
We propose a new algorithm for pattern recognition that outputs some measures of “reliability” for every prediction made, in contrast to the current algorithms that output “bare” predictions only. Our method uses a rule similar to that of nearest neighbours to infer predictions; thus its predictive performance is close to that of nearest neighbours, while the measures of confidence it outputs provide practically useful information for individual predictions.
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Proedrou, K., Nouretdinov, I., Vovk, V., Gammerman, A. (2002). Transductive Confidence Machines for Pattern Recognition. In: Elomaa, T., Mannila, H., Toivonen, H. (eds) Machine Learning: ECML 2002. ECML 2002. Lecture Notes in Computer Science(), vol 2430. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36755-1_32
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DOI: https://doi.org/10.1007/3-540-36755-1_32
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