Multi-output regression using a locally regularised orthogonal least square algorithm
The paper proposes a locally regularised orthogonal least squares (LROLS) algorithm for constructing sparse multi-output regression models that generalise well. By associating each regressor in the regression model with an individual regularisation parameter, the ability for the multi-output orthogo...
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| Format: | Article |
| Language: | English |
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2002-08.
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| Online Access: | Get fulltext Get fulltext |
| LEADER | 00861 am a22001333u 4500 | ||
|---|---|---|---|
| 001 | 256833 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Chen, S. |e author |
| 245 | 0 | 0 | |a Multi-output regression using a locally regularised orthogonal least square algorithm |
| 260 | |c 2002-08. | ||
| 856 | |z Get fulltext |u https://eprints.soton.ac.uk/256833/1/iee_visp2002_149_4.pdf | ||
| 856 | |z Get fulltext |u https://eprints.soton.ac.uk/256833/2/mulreg.ps | ||
| 520 | |a The paper proposes a locally regularised orthogonal least squares (LROLS) algorithm for constructing sparse multi-output regression models that generalise well. By associating each regressor in the regression model with an individual regularisation parameter, the ability for the multi-output orthogonal least squares (OLS) model selection to produce a parsimonious model with good generalisation performance is greatly enhanced. | ||
| 655 | 7 | |a Article | |