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10.1109/ICMLA.2014.64 http://scihub22266oqcxt.onion/10.1109/ICMLA.2014.64 C4826063!4826063 !27066596     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 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\27066596 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Proc+Int+Conf+Mach+Learn+Appl 2014 ; 2014 (ä): 362-366 Nephropedia Template TP {{tp|p=27066596 |t=2014. ExpertBayes: Automatically refining manually built Bayesian networks |pdf=|usr=}}{{27066596 }} gab.com Text ExpertBayes: Automatically refining manually built Bayesian networks JO: Proc Int Conf Mach Learn Appl http://www.kidney.de/pget.php?&tw=1&pmid=27066596 #FOAvip Bayesian network structures are usually built using only the data and starting from an empty network or from a naïve Bayes structure. Very often, in some domains, like medicine, a prior structure knowledge is already known. This structure can be automatically or manually refined in search for better performance models. In this work, we take Bayesian networks built by specialists and show that minor perturbations to this original network can yield better classifiers with a very small computational cost, while maintaining most of the intended meaning of the original model. Twit Text FOAVip ExpertBayes: Automatically refining manually built Bayesian networks ????Proc Int Conf Mach Learn Appl http://www.kidney.de/pget.php?&tw=1&pmid=27066596 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27066596 #FOAvip English Wikipedia <ref>{{Cite pmid|27066596 }}</ref> ExpertBayes: Automatically refining manually built Bayesian networks #MMPMID27066596 Almeida E ; Ferreira P ; Vinhoza T ; Dutra I ; Li J ; Wu Y ; Burnside E Proc Int Conf Mach Learn Appl 2014[Dec]; 2014 (ä): 362-366 PMID27066596 show ga Bayesian network structures are usually built using only the data and starting from an empty network or from a naïve Bayes structure. Very often, in some domains, like medicine, a prior structure knowledge is already known. This structure can be automatically or manually refined in search for better performance models. In this work, we take Bayesian networks built by specialists and show that minor perturbations to this original network can yield better classifiers with a very small computational cost, while maintaining most of the intended meaning of the original model. äExpertBayes: Automatically refining manually built Bayesian networks (epmc).pdf DeepDyve Pubget Overpricing