Large scale multi-label learning using Gaussian processes

DOI10.1007/s10994-021-05952-5OpenAlexW3155957385MaRDI QIDQ2051297

Petros Dellaportas, Michalis K. Titsias, Aristeidis Panos

Publication date: 24 November 2021

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

Full work available at URL: https://doi.org/10.1007/s10994-021-05952-5

zbMATH Keywords

Gaussian processBayesian nonparametricsvariational inferencemulti-label learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Large scale multi-output multi-class classification using Gaussian processes

Uses Software

• ML-KNN
• word2vec
• FastXML
• AnnexML
• DiSMEC
• WSABIE
• CRAFTML

Cites Work

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• Bayesian multi-instance multi-label learning using Gaussian process prior
• ML-KNN: A lazy learning approach to multi-label learning
• Bonsai: diverse and shallow trees for extreme multi-label classification
• Data scarcity, robustness and extreme multi-label classification
• Sparse On-Line Gaussian Processes
• Kernels for Vector-Valued Functions: A Review
• CRAFTML