Kernel-based Data Modelling Using Orthogonal Least Squares Selection with Local Regularisation
نویسنده
چکیده
Combining orthogonal least squares (OLS) model selection with local regularisation or smoothing leads to efficient sparse kernel-based data modelling. By assigning each orthogonal weight in the regression model with an individual regularisation parameter, the ability for the OLS model selection to produce a very parsimonious model with excellent generalisation performance is greatly enhanced.
منابع مشابه
Locally Regularised Orthogonal Least Squares Algorithm for the Construction of Sparse Kernel Regression Models
The paper proposes to combine an orthogonal least squares (OLS) model selection with local regularisation for efficient sparse kernel data modelling. By assigning each orthogonal weight in the regression model with an individual regularisation parameter, the ability for the OLS model selection to produce a very parsimonious model with excellent generalisation performance is greatly enhanced.
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