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- W2143042791 abstract "We propose extensions of the classical JSM-method and the Naive Bayesian classifier for the case of triadic relational data. We performed a series of experiments on various types of data (both real and synthetic) to estimate quality of classification techniques and compare them with other classification algorithms that generate hypotheses, e.g. ID3 and Random Forest. In addition to classification precision and recall we also evaluated the time performance of the proposed methods." @default.
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- W2143042791 date "2014-08-01" @default.
- W2143042791 modified "2023-09-23" @default.
- W2143042791 title "Learning Hypotheses from Triadic Labeled Data" @default.
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- W2143042791 doi "https://doi.org/10.1109/wi-iat.2014.136" @default.
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