Pranking with Ranking

By Koby Crammer, Yoram Singer. Neural Information Processing Systems, 2001

723 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/f4658b7c2c2436312b6d9cbf22c2f1625f7b76b1

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In Economics and Econometrics

In Information Systems

In Management Science and Operations Research

Citations in the corpus

Learning to rank for information retrieval (2009, neutral): “In recent years, learning to rank has become a very hot research direction in IR, and a large number of learning-to-rank algorithms have been proposed, such as [9, 13, 14, 16, 17, 26, 29, 33, 34, 47, 49, 59, 63, 73, 78, 90, 97, 99, 102, 114, 119, 122, 129, 134, 136].”
On the Consistency of Ordinal Regression Methods (2014, neutral): “Examples of ordinal scales include (“do-not-bother” ≺ “only-if-you-must” ≺ “good” ≺ “very-good” ≺ “run-to-see”) in movie ratings [7], (“absent” ≺ “mild” ≺ “severe”) for the symptoms of a physical disease [1] and the NRS-11 numeric rating scale for clinical pain measurement [9].”
Learning to rank: from pairwise approach to listwise approach (2007, neutral): “For other approaches, see (Shashua & Levin, 2002; Crammer & Singer, 2001; Lebanon & Lafferty, 2002), for example.”
BoltzRank: learning to maximize expected ranking gain (2009, background): “Introduction Ranking in general, and in particular web document ranking, has received a lot of attention recently primarily due to its direct application in search engines.”
New approaches to support vector ordinal regression (2005, methodology): “Frank and Hall (2001) converted an ordinal regression problem into nested binary classification problems that encode the ordering of the original ranks and then organized the results of these binary classifiers in some ad hoc way for prediction.”
Learning to rank code examples for code search engines (2017, background): “As stated by Li (2011), learning to rank algorithms can fall into three categories: pointwise approaches (Crammer and Singer 2001; Li et al. 2008), pairwise approaches (Burges et al. 2005; Freund et al. 2003; Herbrich et al. 2000), and listwise approaches (Cao et al. 2007; Xu and Li 2007).”
The scoring of poses in protein-protein docking: current capabilities and future directions (2013, background): “For instance, when ranking is undertaken in the pointwise approach, it can be reformulated as a classification, regression or ordinal classification problem [125-128].”
Support Vector Ordinal Regression (2007, methodology): “As for sequential learning, Crammer and Singer (2002) proposed a proceptron-based online algorithm for rank prediction, known as the PRank algorithm.”
Coactive Learning (2015, background): “Ordinal regression (e.g., Crammer & Singer, 2001) assumes training examples (x, y), where y is a rank.”
Rank consistent ordinal regression for neural networks with application to age estimation (2019, methodology): “Early work in this regard includes the use of perceptrons (Crammer and Singer, 2002; Shen and Joshi, 2005) and support vector machines (Herbrich et al., 1999; Shashua and Levin, 2003; Rajaram et al., 2003; Chu and Keerthi, 2005).”
Ordinal Regression with Multiple Output CNN for Age Estimation (2016, methodology): “In [9], the perceptron ranking (PRank) algorithm proposed by Crammer and Singer is to generalize the online perceptron algorithm with multiple thresholds for ordinal regression.”
Gaussian Processes for Ordinal Regression (2005, methodology): “Shashua and Levin (2003) generalized the support vector formulation by finding multiple thresholds to define parallel discriminant hyperplanes for ordinal scales, and reported that the performance of the support vector approach is better than that of the on-line algorithm (Crammer and Singer, 2002).”
Discriminative Reranking for Machine Translation (2004, methodology): “One is the PRank algorithm, a variant of the perceptron algorithm, that uses multiple biases to represent the boundaries between every two consecutive ranks (Crammer and Singer, 2001; Harrington, 2003).”
Learning Dual Retrieval Module for Semi-supervised Relation Extraction (2019, methodology): “e let qϕ(x,y)output a score to estimate the degree of relevance of the pair (x,y). In literature, there are mainly two types of approaches to train such retrieval models, i.e., the pointwise approach [8, 18] and the pairwise approach [5, 6], and we tried both in our experiment. Next, we introduce th”
Generalization Error Bounds for Collaborative Prediction with Low-Rank Matrices (2004, methodology): “Generalization error bounds (and related online learning bounds) were previously discussed for collaborative prediction applications, but only when prediction was done for each user separately, using a feature-based method, with the other user’s preferences as features [5, 6].”
Deep Ordinal Regression Network for Monocular Depth Estimation (2018, background): “For example, Shashua and Levin [53] handled multiple thresholds by developing a new SVM. Cam-mer and Singer [9] generalized the online perceptron algorithms with multiple thresholds to do ordinal regression.”
Direct Learning to Rank and Rerank (2018, neutral): “There are also algorithms for ordinal regression , which is a related but different learning problem (Li et al, 2007; Crammer et al, 2001; Herbrich et al, 1999), and listwise approaches to ranking (Cao et al, 2007; Xia et al, 2008; Xu and Li, 2007; Yue et al, 2007).”