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• W2016421154 abstract "A unified approach is proposed for data modelling that includes supervised regression and classification applications as well as unsupervised probability density function estimation. The orthogonal-least-squares regression based on the leave-one-out test criteria is formulated within this unified data-modelling framework to construct sparse kernel models that generalise well. Examples from regression, classification and density estimation applications are used to illustrate the effectiveness of this generic data-modelling approach for constructing parsimonious kernel models with excellent generalisation capability." @default.
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• W2016421154 date "2009-06-01" @default.
• W2016421154 modified "2023-10-18" @default.
• W2016421154 title "Orthogonal-least-squares regression: A unified approach for data modelling" @default.
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• W2016421154 doi "https://doi.org/10.1016/j.neucom.2008.10.002" @default.
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