Large Margin Rank Boundaries for Ordinal Regression

By Darren Gehring, Thore Graepel. 2000

1,038 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/958f001b6f348f7c353260b289bed185fffac847

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Citations in the corpus

A scalable modular convex solver for regularized risk minimization (2007, background): “…solve (1)) include Support Vector regression [41], novelty detection [33], Huber’s robust regression, quantile regression [37], ordinal regression [21], ranking [15], maximization of multivariate performance measures [24], structured estimation [38, 40], Gaussian Process regression [43],…”
Learning Scoring Functions with Order-Preserving Losses and Standardized Supervision (2011, background): “For instance, some popular surrogate losses are based on pairwise comparisons (Weston & Watkins, 1999; Herbrich et al., 2000; Freund et al., 2003; Joachims, 2002); they are deﬁned as convex upper bounds on the pairwise disagreement , an evaluation metric which counts the number of mis-ordered pairs…”
Learning Fine-Grained Expressions to Solve Math Word Problems (2017, methodology): “We apply the Ranking SVM (Herbrich et al., 2000) to minimize a regularized margin-based pairwise loss.”
Large Margin Methods for Structured and Interdependent Output Variables (2005, background): “The same is true also for other ranking algorithms (Cohen et al., 1999; Herbrich et al., 2000; Schapire and Singer, 2000; Crammer and Singer, 2002; Joachims, 2002).”
Support Vector Ordinal Regression (2007, methodology): “Herbrich et al. (2000) applied the principle of Structural Risk Minimization (Vapnik , 1995) to ordinal regression leading to a new distribution-independent learning algorithm based on a loss function between pairs of ranks.”
Ranking of text documents using TF-IDF weighting and association rules mining (2018, methodology): “They are based on techniques such as neural networks [4], genetic programming [5] and support vector machines [6,7] to learn the ranking model.”
Gaussian Processes for Ordinal Regression (2005, methodology): “Herbrich et al. (2000) applied the principle of Structural Risk Minimization (Vapnik, 1995) to ordinal regression leading to a new distribution-independent learning algorithm based on a loss function between pairs of ranks.”
Yahoo! Learning to Rank Challenge Overview (2010, methodology): “We considered the linear version of this pairwise ranking algorithm first introduced in (Herbrich et al., 2000).”
A support vector method for multivariate performance measures (2005, methodology): “For the case of binary classification with unbiased hyperplanes1, SVMs learn a classifier h(x) = sign [ wT x ] by solving the following optimization problem.”
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models (2017, methodology): “In addition to the logistic function log(1+exp(−z)), it is possible to make use of other pairwise ranking loss functions [7], such as the hinge function (1 − z)+ (as used in Ranking SVM [16]) and the exponential function exp(−z) (as used in RankBoost [11]), to dene the probability D(〈du ,dv 〉|q).”
A support vector method for optimizing average precision (2007, methodology): “3.2 Finding the Most Violated Constraint Using OP 1 and optimizing to ROCArea loss (.roc ), the documents ranked before it, as opposed to j -1.”
Query chains: learning to rank from implicit feedback (2005, methodology): “On the one hand, a number of methods in ordinal regression use explicit feedback to learn to rank, such as work by Crammer and Singer [7], Rajaram et al. [22] and Herbrich et al. [13].”
Introduction to Information Retrieval (2008, neutral): “Machine learning approaches to ordinal regression appear in (Herbrich et al. 2000) and (Burges et al.”
Ranking with Large Margin Principle: Two Approaches (2002, background): “This favorably compares to the O(l2) required by the recent SYM approach to ordinal regression introduced in [7] or the kl required by the general multi-class approach to SYM [4,8].”
Learning to Rank Using User Clicks and Visual Features for Image Retrieval (2015, methodology): “To solve this problem, a method called the pairwise approach [8], [9] has been proposed and successfully used in document retrieval.”
Preference-learning based Inverse Reinforcement Learning for Dialog Control (2012, methodology): “In this study, we adopt pairwise preference [9] to represent the preference model for the sake of simplicity of implementation.”
Ranking Measures and Loss Functions in Learning to Rank (2009, background): “The key to obtaining this result is to model ranking as a sequence of classification tasks, and define a so-calledessential loss for ranking as the weighted sum of the classification errors of individual tasks in the sequence.”