Name
Abstract | ||
|---|---|---|
This paper presents a new variant of the perceptron algorithm using selective committee averaging (or voting). We apply this agorithm to the problem of learning ranking functions for document retrieval, known as the "Learning to Rank" problem. Most previous algorithms proposed to address this problem focus on minimizing the number of misranked document pairs in the training set. The committee perceptron algorithm improves upon existing solutions by biasing the final solution towards maximizing an arbitrary rank-based performance metrics. This method performs comparably or better than two state-of-the-art rank learning algorithms, and also provides significant training time improvements over those methods, showing over a 45-fold reduction in training time compared to ranking SVM |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1145/1341531.1341542 | WSDM |
Keywords | Field | DocType |
ranking svm,training set,significant training time improvement,perceptron algorithm,training time,problem focus,document ranking function,previous algorithm,committee perceptron algorithm,document retrieval,misranked document pair,learning to rank | Training set,Data mining,Learning to rank,Ranking,Voting,Ranking SVM,Computer science,Artificial intelligence,Document retrieval,Perceptron,Machine learning | Conference |
Citations | PageRank | References |
20 | 0.90 | 28 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jonathan Elsas | 1 | 399 | 18.04 |
| Vitor R. Carvalho | 2 | 672 | 36.38 |
| Jaime G. Carbonell | 3 | 5019 | 724.15 |