Multi-target prediction: a unifying view on problems and methods

DOI10.1007/s10618-018-0595-5zbMath1464.62399arXiv1809.02352OpenAlexW2891165828MaRDI QIDQ2218329

Eyke Hüllermeier, Krzysztof Dembczyński, Willem Waegeman

Publication date: 15 January 2021

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1809.02352

zbMATH Keywords

collaborative filteringmultivariate regressionmulti-label classificationpairwise learningmulti-task learningzero-shot learningdyadic predictionmulti-target prediction (MTP)

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Hypothesis testing in multivariate analysis (62H15) General nonlinear regression (62J02) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (5)

Generalized vec trick for fast learning of pairwise kernel models ⋮ Multi-target prediction for dummies using two-branch neural networks ⋮ Distributed solver for linear matrix inequalities: an optimization perspective ⋮ Learning-augmented heuristics for scheduling parallel serial-batch processing machines ⋮ Classifier Chains: A Review and Perspectives

Uses Software

• NMF
• ElemStatLearn
• softImpute
• word2vec
• DeCAF
• OverFeat
• HCP

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• A survey of hierarchical classification across different application domains
• Multi-output learning via spectral filtering
• On label dependence and loss minimization in multi-label classification
• Multi-dimensional classification with Bayesian networks
• Learning intransitive reciprocal relations with kernel methods
• Decision trees for hierarchical multi-label classification
• Reduced-rank regression for the multivariate linear model
• The ubiquitous Kronecker product
• Dyad ranking using Plackett-Luce models based on joint feature representations
• Multi-target regression via input space expansion: treating targets as inputs
• Combining instance-based learning and logistic regression for multilabel classification
• Efficient regularized least-squares algorithms for conditional ranking on relational data
• Label ranking by learning pairwise preferences
• Exact matrix completion via convex optimization
• Kernels for Vector-Valued Functions: A Review
• Multivariate regression analysis and canonical variates
• 10.1162/153244304322765658
• Multilabel Classification with Principal Label Space Transformation
• Machine Learning: ECML 2004
• Low-rank matrix completion using alternating minimization
• The elements of statistical learning. Data mining, inference, and prediction

This page was built for publication: Multi-target prediction: a unifying view on problems and methods