A survey on multi‐output regression
By Hanen Borchani, Gherardo Varando, Concha Bielza, Pedro Larrañaga. 2015
638 citations recorded by the source corpus.
In recent years, a plethora of approaches have been proposed to deal with the increasingly challenging task of multi‐output regression. This study provides a survey on state‐of‐the‐art multi‐output regression methods, that are categorized as problem transformation and algorithm adaptation methods. In addition, we present the mostly used performance evaluation measures, publicly available data sets for multi‐output regression real‐world problems, as well as open‐source software frameworks. WIREs Data Mining Knowl Discov 2015, 5:216–233. doi: 10.1002/widm.1157
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Bharath K. Sriperumbudur
- Arthur Gretton
- Barnab'as P'oczos
- Bernhard Schölkopf
- Zoltán Szabó
- Kenji Fukumizu
- Chin-Hui Lee
- Krikamol Muandet
In Computer Vision and Pattern Recognition
- Bharath K. Sriperumbudur
- Arthur Gretton
- Barnab'as P'oczos
- Jun Du
- Zoltán Szabó
- Sabato Marco Siniscalchi
- Chin-Hui Lee
- Bernhard Schölkopf
Citations in the corpus
- Learning Theory for Distribution Regression (2014, background): “, 2015); for a recent review on multi-output prediction methods see (Álvarez et al., 2011; Borchani et al., 2015).”
- On Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression (2020, background): “M EAN absolute error (MAE), originated from a measure of average error [1], is often employed in assessing vector-to-vector (a.k.a. multivariate) regression models [2].”