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Predicting the Best System Parameter Configuration: the (Per Parameter Learning) PPL method

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Academic year: 2021

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Table 1. Candidate training examples for two queries which are used in training two classifiers that will predict the best values of the system parameters individually (P1 and P2) and will optimize the average precision.
Table 2. System parameters with the corresponding values
Table 3 shows the best kernel method name with the best gamma and polynomial degree value for each considered IR system parameter, using different numbers of top predefined systems (N)
Fig. 2. MAP - A comparison between the best predefined system, the PPL method, the predefined system classifier, and the ground truth method

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