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http://scihub22266oqcxt.onion/ C5354374!5354374!28316509     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Mach+Learn+Res 2015 ; 16 (ä): 1579-606 Nephropedia Template TP {{tp|p=28316509|t=2015. Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery? |pdf=|usr=}}{{28316509}} gab.com Text Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery JO: J Mach Learn Res http://www.kidney.de/pget.php?&tw=1&pmid=28316509 #FOAvip We propose a calibrated multivariate regression method named CMR for fitting high dimensional multivariate regression models. Compared with existing methods, CMR calibrates regularization for each regression task with respect to its noise level so that it simultaneously attains improved finite-sample performance and tuning insensitiveness. Theoretically, we provide sufficient conditions under which CMR achieves the optimal rate of convergence in parameter estimation. Computationally, we propose an efficient smoothed proximal gradient algorithm with a worst-case numerical rate of convergence O(1/?), where ? is a pre-specified accuracy of the objective function value. We conduct thorough numerical simulations to illustrate that CMR consistently outperforms other high dimensional multivariate regression methods. We also apply CMR to solve a brain activity prediction problem and find that it is as competitive as a handcrafted model created by human experts. The R package camel implementing the proposed method is available on the Comprehensive R Archive Network http://cran.r-project.org/web/packages/camel/. Twit Text FOAVip Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery ????J Mach Learn Res http://www.kidney.de/pget.php?&tw=1&pmid=28316509 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28316509 #FOAvip English Wikipedia <ref>{{Cite pmid|28316509}}</ref> Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery? #MMPMID28316509 Liu H; Wang L; Zhao T J Mach Learn Res 2015[Aug]; 16 (ä): 1579-606 PMID28316509show ga We propose a calibrated multivariate regression method named CMR for fitting high dimensional multivariate regression models. Compared with existing methods, CMR calibrates regularization for each regression task with respect to its noise level so that it simultaneously attains improved finite-sample performance and tuning insensitiveness. Theoretically, we provide sufficient conditions under which CMR achieves the optimal rate of convergence in parameter estimation. Computationally, we propose an efficient smoothed proximal gradient algorithm with a worst-case numerical rate of convergence O(1/?), where ? is a pre-specified accuracy of the objective function value. We conduct thorough numerical simulations to illustrate that CMR consistently outperforms other high dimensional multivariate regression methods. We also apply CMR to solve a brain activity prediction problem and find that it is as competitive as a handcrafted model created by human experts. The R package camel implementing the proposed method is available on the Comprehensive R Archive Network http://cran.r-project.org/web/packages/camel/. äCalibrated Multivariate Regression with Application to Neural Semantic(epmc).pdf DeepDyve Pubget Overpricing