Multi-target regression via input space expansion: treating targets as inputs
From MaRDI portal
Publication:1689552
DOI10.1007/s10994-016-5546-zzbMath1454.68134arXiv1211.6581OpenAlexW3124484778WikidataQ62864526 ScholiaQ62864526MaRDI QIDQ1689552
Ioannis Vlahavas, Eleftherios Spyromitros-Xioufis, William Groves, Grigorios Tsoumakas
Publication date: 12 January 2018
Published in: Machine Learning (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1211.6581
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)
Related Items (20)
Multi-label classification with weighted classifier selection and stacked ensemble ⋮ Ensembles for multi-target regression with random output selections ⋮ JGPR: a computationally efficient multi-target Gaussian process regression algorithm ⋮ Alternating DCA for reduced-rank multitask linear regression with covariance matrix estimation ⋮ Progressive random \(k\)-labelsets for cost-sensitive multi-label classification ⋮ An ensemble-adaptive tree-based chain framework for multi-target regression problems ⋮ Multi-target regression via input space expansion: treating targets as inputs ⋮ Scalable Model-Free Feature Screening via Sliced-Wasserstein Dependency ⋮ Learning-augmented heuristics for scheduling parallel serial-batch processing machines ⋮ Model averaging for sparse seemingly unrelated regression using Bayesian networks among the errors ⋮ Multi-label classification via multi-target regression on data streams ⋮ Incremental predictive clustering trees for online semi-supervised multi-target regression ⋮ Fast and accurate pseudoinverse with sparse matrix reordering and incremental approach ⋮ Multi-target prediction: a unifying view on problems and methods ⋮ Orthogonal canonical correlation analysis and applications ⋮ A deep multitask learning approach for air quality prediction ⋮ Regularization-based model tree for multi-output regression ⋮ Prediction of arch dam deformation via correlated multi-target stacking ⋮ Multi-target support vector regression via correlation regressor chains ⋮ Feature ranking for multi-target regression
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Bagging predictors
- On label dependence and loss minimization in multi-label classification
- First order regression
- Efficient Monte Carlo methods for multi-dimensional learning with classifier chains
- Convex multi-task feature learning
- Reduced-rank regression for the multivariate linear model
- Multi-target regression via input space expansion: treating targets as inputs
- Combining instance-based learning and logistic regression for multilabel classification
- Kernels for Vector-Valued Functions: A Review
- Modern Multivariate Statistical Techniques
- Approximations of the critical region of the fbietkan statistic
- Multivariate regression analysis and canonical variates
- Not all traces on the circle come from functions of least gradient in the disk
- Multilabel Classification with Principal Label Space Transformation
- A Dirty Model for Multiple Sparse Regression
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
- The elements of statistical learning. Data mining, inference, and prediction
- Stochastic gradient boosting.
This page was built for publication: Multi-target regression via input space expansion: treating targets as inputs