Learning to rank using gradient descent (2005)

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by Chris Burges , Tal Shaked , Erin Renshaw , Matt Deeds , Nicole Hamilton , Greg Hullender
Venue:In ICML
Citations:388 - 16 self

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Learning to Rank using Gradient Descent Keywords: ranking, gradient descent, neural networks, probabilistic cost functions – Chris Burges, Tal Shaked, Erin Renshaw, Matt Deeds, Nicole Hamilton, Greg Hullender
1 Ranking As Function Approximation – Christopher J. C. Burges - 2006
538 An Efficient Boosting Algorithm for Combining Preferences – Raj Dharmarajan Iyer , Jr. - 1999
892 Optimizing Search Engines using Clickthrough Data – Thorsten Joachims - 2002
725 Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods – John C. Platt - 1999
2084 Online Learning with Kernels – Jyrki Kivinen, Alexander J. Smola, Robert C. Williamson - 2003
1156 Machine Learning in Automated Text Categorization – Fabrizio Sebastiani - 2002
705 Improved Boosting Algorithms Using Confidence-rated Predictions – Robert E. Schapire , Yoram Singer - 1999
827 Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions – Gediminas Adomavicius, Alexander Tuzhilin - 2005