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lasso_mex function  (MathWorks Inc)


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    MathWorks Inc lasso_mex function
    Overview over hardware and software requirements of the different functions for estimating L1-regularized linear models. The <t> lasso function </t> is part of the Statistics and Machine Learning Toolbox and only included here for benchmarking purposes (see main text). The lasso_mex, lasso_mexcuda and lasso_gpu functions introduced here are specifically optimized for the mass-univariate analysis approach and depend on different hardware and software configurations
    Lasso Mex Function, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/lasso_mex function/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    lasso_mex function - by Bioz Stars, 2026-04
    90/100 stars

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    1) Product Images from "Fast Estimation of L1-Regularized Linear Models in the Mass-Univariate Setting"

    Article Title: Fast Estimation of L1-Regularized Linear Models in the Mass-Univariate Setting

    Journal: Neuroinformatics

    doi: 10.1007/s12021-020-09489-1

    Overview over hardware and software requirements of the different functions for estimating L1-regularized linear models. The lasso function is part of the Statistics and Machine Learning Toolbox and only included here for benchmarking purposes (see main text). The lasso_mex, lasso_mexcuda and lasso_gpu functions introduced here are specifically optimized for the mass-univariate analysis approach and depend on different hardware and software configurations
    Figure Legend Snippet: Overview over hardware and software requirements of the different functions for estimating L1-regularized linear models. The lasso function is part of the Statistics and Machine Learning Toolbox and only included here for benchmarking purposes (see main text). The lasso_mex, lasso_mexcuda and lasso_gpu functions introduced here are specifically optimized for the mass-univariate analysis approach and depend on different hardware and software configurations

    Techniques Used: Software



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