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10.1007/s10444-021-09875-6 http://scihub22266oqcxt.onion/10.1007/s10444-021-09875-6 34220169!8233636!34220169     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34220169&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       Adv+Comput+Math 2021 ; 47 (4): 51 Nephropedia Template TP {{tp|p=34220169|t=2021. Learning via variably scaled kernels |pdf=|usr=}}{{34220169}} gab.com Text Learning via variably scaled kernels JO: Adv Comput Math http://www.kidney.de/pget.php?&tw=1&pmid=34220169 #FOAvip We investigate the use of the so-called variably scaled kernels (VSKs) for learning tasks, with a particular focus on support vector machine (SVM) classifiers and kernel regression networks (KRNs). Concerning the kernels used to train the models, under appropriate assumptions, the VSKs turn out to be more expressive and more stable than the standard ones. Numerical experiments and applications to breast cancer and coronavirus disease 2019 (COVID-19) data support our claims. For the practical implementation of the VSK setting, we need to select a suitable scaling function. To this aim, we propose different choices, including for SVMs a probabilistic approach based on the naive Bayes (NB) classifier. For the classification task, we also numerically show that the VSKs inspire an alternative scheme to the sometimes computationally demanding feature extraction procedures. Twit Text FOAVip Learning via variably scaled kernels ????Adv Comput Math http://www.kidney.de/pget.php?&tw=1&pmid=34220169 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34220169 #FOAvip English Wikipedia <ref>{{Cite pmid|34220169}}</ref> Learning via variably scaled kernels #MMPMID34220169 Campi C; Marchetti F; Perracchione E Adv Comput Math 2021[]; 47 (4): 51 PMID34220169show ga We investigate the use of the so-called variably scaled kernels (VSKs) for learning tasks, with a particular focus on support vector machine (SVM) classifiers and kernel regression networks (KRNs). Concerning the kernels used to train the models, under appropriate assumptions, the VSKs turn out to be more expressive and more stable than the standard ones. Numerical experiments and applications to breast cancer and coronavirus disease 2019 (COVID-19) data support our claims. For the practical implementation of the VSK setting, we need to select a suitable scaling function. To this aim, we propose different choices, including for SVMs a probabilistic approach based on the naive Bayes (NB) classifier. For the classification task, we also numerically show that the VSKs inspire an alternative scheme to the sometimes computationally demanding feature extraction procedures. äLearning via variably scaled kernels (epmc).pdf DeepDyve Pubget Overpricing