Dyad ranking using Plackett-Luce models based on joint feature representations

DOI10.1007/s10994-017-5694-9zbMath1464.62232OpenAlexW2783218948MaRDI QIDQ1640577

Dirk Schäfer, Eyke Hüllermeier

Publication date: 14 June 2018

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-017-5694-9

zbMATH Keywords

neural networksPlackett-Luce modellabel rankingpreference learningmultidimensional unfoldingdyad ranking

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Statistical ranking and selection procedures (62F07) Neural nets and related approaches to inference from stochastic processes (62M45)

Related Items

Optimizing non-decomposable measures with deep networks ⋮ Multi-target prediction: a unifying view on problems and methods ⋮ Bayesian ranking for strategy scheduling in automated theorem provers

Uses Software

• L-BFGS
• UCI-ml
• smacof
• TensorFlow
• Caffe
• ImageNet
• RLScore
• Caltech-256
• AlexNet

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