Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The note proposes an efficient nonlinear identification algorithm by combining a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximized model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious model with excellent generalization performance. The D-optimality design criterion further enhances the model efficiency and robustness. An added advantage is that the user only needs to specify a ighting for the D-optimality cost in the combined model selecting criterion and the entire model construction procedure becomes automatic. The value of this ighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.
['Sheng Chen', 'Xia Hong', 'Chris J. Harris']
I am writing a research paper on a idea Sparse kernel regression modeling using combined locally regularized orthogonal least squares and D-optimality experimental design
[{"from": "human", "value": "I am writing a research paper on a idea Sparse kernel regression modeling using combined locally regularized orthogonal least squares and D-optimality experimental design"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The note proposes an efficient nonlinear identification algorithm by combining a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximized model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious model with excellent generalization performance. The D-optimality design criterion further enhances the model efficiency and robustness. An added advantage is that the user only needs to specify a ighting for the D-optimality cost in the combined model selecting criterion and the entire model construction procedure becomes automatic. The value of this ighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values."}]