We describe an algorithm for choosing term weights to maximize average precision.
Direct Maximization of Average Precision by Hill-Climbing, with a Comparison to a Maximum Entropy Approach
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We describe an algorithm for choosing term weights to maximize average precision. The algorithm performs successive exhaustive searches through single directions in weight space. It makes use of a novel technique for considering all possible values of average precision that arise in searching for a maximum in a given direction. We apply the algorithm and compare this algorithm to a maximum entropy approach.