We link nuclear force with gravity. We use statistical entropy to link fine-structure constant （ct） and cosmological constant, showing mystical number 137 （as reciprocal of increasing entropy of the universe） as n...We link nuclear force with gravity. We use statistical entropy to link fine-structure constant （ct） and cosmological constant, showing mystical number 137 （as reciprocal of increasing entropy of the universe） as negative entropy needed for life to exist. If our computational route applies to the physical universe, it should apply to life. Molecular biology is searching for the fundamental source of information that would link to the information in DNA.展开更多
There are two fundamental goals in statistical learning： identifying relevant predictors and ensuring high prediction accuracy. The first goal, by means of variable selection, is of particular importance when the tru...There are two fundamental goals in statistical learning： identifying relevant predictors and ensuring high prediction accuracy. The first goal, by means of variable selection, is of particular importance when the true underlying model has a sparse representation. Discovering relevant predictors can enhance the performance of the prediction for the fitted model. Usually an estimate is considered desirable if it is consistent in terms of both coefficient estimate and variable selection. Hence, before we try to estimate the regression coefficients β , it is preferable that we have a set of useful predictors m hand. The emphasis of our task in this paper is to propose a method, in the aim of identifying relevant predictors to ensure screening consistency in variable selection. The primary interest is on Orthogonal Matching Pursuit（OMP）.展开更多
文摘We link nuclear force with gravity. We use statistical entropy to link fine-structure constant （ct） and cosmological constant, showing mystical number 137 （as reciprocal of increasing entropy of the universe） as negative entropy needed for life to exist. If our computational route applies to the physical universe, it should apply to life. Molecular biology is searching for the fundamental source of information that would link to the information in DNA.
文摘There are two fundamental goals in statistical learning： identifying relevant predictors and ensuring high prediction accuracy. The first goal, by means of variable selection, is of particular importance when the true underlying model has a sparse representation. Discovering relevant predictors can enhance the performance of the prediction for the fitted model. Usually an estimate is considered desirable if it is consistent in terms of both coefficient estimate and variable selection. Hence, before we try to estimate the regression coefficients β , it is preferable that we have a set of useful predictors m hand. The emphasis of our task in this paper is to propose a method, in the aim of identifying relevant predictors to ensure screening consistency in variable selection. The primary interest is on Orthogonal Matching Pursuit（OMP）.
