نتایج جستجو برای: روش lasso

تعداد نتایج: 374083  

2007
Hansheng Wang

We propose a method of least squares approximation (LSA) for unified yet simple LASSO estimation. Our general theoretical framework includes ordinary least squares, generalized linear models, quantile regression, and many others as special cases. Specifically, LSA can transfer many different types of LASSO objective functions into their asymptotically equivalent least-squares problems. Thereaft...

Journal: :Journal of Computational and Graphical Statistics 2016

Journal: :Quality and Reliability Engineering International 2016

Journal: :Techniques & culture 1993

Journal: :Journal of Al-Qadisiyah for computer science and mathematics 2019

Journal: :SSRN Electronic Journal 2016

Journal: :The annals of applied statistics 2011
Sijian Wang Bin Nan Saharon Rosset Ji Zhu

We propose a computationally intensive method, the random lasso method, for variable selection in linear models. The method consists of two major steps. In step 1, the lasso method is applied to many bootstrap samples, each using a set of randomly selected covariates. A measure of importance is yielded from this step for each covariate. In step 2, a similar procedure to the first step is implem...

Journal: :Neural networks : the official journal of the International Neural Network Society 2010
Junbin Gao Paul Wing Hing Kwan Daming Shi

Kernelized LASSO (Least Absolute Selection and Shrinkage Operator) has been investigated in two separate recent papers [Gao, J., Antolovich, M., & Kwan, P. H. (2008). L1 LASSO and its Bayesian inference. In W. Wobcke, & M. Zhang (Eds.), Lecture notes in computer science: Vol. 5360 (pp. 318-324); Wang, G., Yeung, D. Y., & Lochovsky, F. (2007). The kernel path in kernelized LASSO. In Internationa...

2010
Laurent El Ghaoui Vivian Viallon Tarek Rabbani

We describe a fast method to eliminate features (variables) in l1-penalized least-square regression (or LASSO) problems. The elimination of features leads to a potentially substantial reduction in running time, especially for large values of the penalty parameter. Our method is not heuristic: it only eliminates features that are guaranteed to be absent after solving the LASSO problem. The featu...

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