Direct Maximization of Average Precision by Hill-Climbing, with a Comparison to a Maximum Entropy Approach
By William T. Morgan, Warren R. Greiff, John C. Henderson. 2004
36 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Statistical and Nonlinear Physics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Donald Metzler
- W. Bruce Croft
- Jamie Callan
- Jimmy J. Lin
- Hamed Zamani
- Chengxiang Zhai
- Zhuyun Dai
- Andrew Yates
In Statistical and Nonlinear Physics
Citations in the corpus
- A Markov random field model for term dependencies (2005, background): “This has been shown to be true experimentally and is known as metric divergence [13].”
- Linear feature-based models for information retrieval (2007, unknown): “This is known as metric divergence [22].”