Surrogate Functions for Maximizing Precision at the Top
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چکیده
Consider a set of 6 points in R × {0, 1}: {(−1, 1), (−1, 1), (−2, 1), (−3, 0), (−3, 0), (−3, 0)}, and suppose we are interested in Prec@1. Note that the optimum model that maximizes prec@1 on these points has a positive sign. We will now show that the model w∗ ∈ R that maximizes the above structural SVM surrogate on these points has a negative sign. On the contrary, let us assume that w∗ has a positive sign, and arrive at a contradiction; we shall consider the following two cases:
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Surrogate Functions for Maximizing Precision at the Top
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