Open AccessProceedings Article
Learning Rankings via Convex Hull Separation
Glenn Fung,Romer Rosales,Balaji Krishnapuram +2 more
- Vol. 18, pp 395-402
TLDR
Experiments indicate that the proposed algorithm for learning ranking functions from order constraints between sets—i.e. classes—of training samples is at least as accurate as the current state-of-the-art and several orders of magnitude faster than current methods.Abstract:
We propose efficient algorithms for learning ranking functions from order constraints between sets—i.e. classes—of training samples. Our algorithms may be used for maximizing the generalized Wilcoxon Mann Whitney statistic that accounts for the partial ordering of the classes: special cases include maximizing the area under the ROC curve for binary classification and its generalization for ordinal regression. Experiments on public benchmarks indicate that: (a) the proposed algorithm is at least as accurate as the current state-of-the-art; (b) computationally, it is several orders of magnitude faster and—unlike current methods—it is easily able to handle even large datasets with over 20,000 samples.read more
Citations
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Book
Learning to Rank for Information Retrieval
TL;DR: Three major approaches to learning to rank are introduced, i.e., the pointwise, pairwise, and listwise approaches, the relationship between the loss functions used in these approaches and the widely-used IR evaluation measures are analyzed, and the performance of these approaches on the LETOR benchmark datasets is evaluated.
Proceedings ArticleDOI
Learning to rank: from pairwise approach to listwise approach
TL;DR: It is proposed that learning to rank should adopt the listwise approach in which lists of objects are used as 'instances' in learning, and introduces two probability models, respectively referred to as permutation probability and top k probability, to define a listwise loss function for learning.
Proceedings ArticleDOI
AdaRank: a boosting algorithm for information retrieval
TL;DR: The proposed novel learning algorithm, referred to as AdaRank, repeatedly constructs 'weak rankers' on the basis of reweighted training data and finally linearly combines the weak rankers for making ranking predictions, which proves that the training process of AdaRank is exactly that of enhancing the performance measure used.
Journal ArticleDOI
Learning to Rank for Information Retrieval
TL;DR: A statistical ranking theory is introduced, which can describe different learning-to-rank algorithms, and be used to analyze their query-level generalization abilities.
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