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http://scihub22266oqcxt.onion/ C4883123!4883123!27239164     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 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 2016 ; 17 (16): 1-26 Nephropedia Template TP {{tp|p=27239164|t=2016. A Consistent Information Criterion for Support Vector Machines in Diverging Model Spaces |pdf=|usr=}}{{27239164}} gab.com Text A Consistent Information Criterion for Support Vector Machines in Diverging Model Spaces JO: J Mach Learn Res http://www.kidney.de/pget.php?&tw=1&pmid=27239164 #FOAvip Information criteria have been popularly used in model selection and proved to possess nice theoretical properties. For classification, Claeskens et al. (2008) proposed support vector machine information criterion for feature selection and provided encouraging numerical evidence. Yet no theoretical justification was given there. This work aims to fill the gap and to provide some theoretical justifications for support vector machine information criterion in both fixed and diverging model spaces. We first derive a uniform convergence rate for the support vector machine solution and then show that a modification of the support vector machine information criterion achieves model selection consistency even when the number of features diverges at an exponential rate of the sample size. This consistency result can be further applied to selecting the optimal tuning parameter for various penalized support vector machine methods. Finite-sample performance of the proposed information criterion is investigated using Monte Carlo studies and one real-world gene selection problem. Twit Text FOAVip A Consistent Information Criterion for Support Vector Machines in Diverging Model Spaces ????J Mach Learn Res http://www.kidney.de/pget.php?&tw=1&pmid=27239164 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27239164 #FOAvip English Wikipedia <ref>{{Cite pmid|27239164}}</ref> A Consistent Information Criterion for Support Vector Machines in Diverging Model Spaces #MMPMID27239164 Zhang X; Wu Y; Wang L; Li R J Mach Learn Res 2016[]; 17 (16): 1-26 PMID27239164show ga Information criteria have been popularly used in model selection and proved to possess nice theoretical properties. For classification, Claeskens et al. (2008) proposed support vector machine information criterion for feature selection and provided encouraging numerical evidence. Yet no theoretical justification was given there. This work aims to fill the gap and to provide some theoretical justifications for support vector machine information criterion in both fixed and diverging model spaces. We first derive a uniform convergence rate for the support vector machine solution and then show that a modification of the support vector machine information criterion achieves model selection consistency even when the number of features diverges at an exponential rate of the sample size. This consistency result can be further applied to selecting the optimal tuning parameter for various penalized support vector machine methods. Finite-sample performance of the proposed information criterion is investigated using Monte Carlo studies and one real-world gene selection problem. äA Consistent Information Criterion for Support Vector Machines in Dive(epmc).pdf DeepDyve Pubget Overpricing