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Query-based learning of acyclic conditional preference networks from noisy data

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Academic year: 2021

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Figure

Figure 1. General scheme of a learning procedure for CP-nets.
Figure 2. Complete CP-net with three variables.
Figure 3. Learning accuracy according to the number of parent p per variable. Datasets are randomly generated except for the real 126 hotels file.
Figure 5. Learning time when the number of variables is fixed (n = 15). We increase the number of objects in the datasets

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