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10.1007/978-3-662-44851-9_4 http://scihub22266oqcxt.onion/10.1007/978-3-662-44851-9_4 C4492338!4492338 !26158123     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26158123 &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       Mach+Learn+Knowl+Discov+Databases 2014 ; 8725 (?): 50-65 Nephropedia Template TP {{tp|p=26158123 |t=2014. Support Vector Machines for Differential Prediction |pdf=|usr=}}{{26158123 }} gab.com Text Support Vector Machines for Differential Prediction JO: Mach Learn Knowl Discov Databases http://www.kidney.de/pget.php?&tw=1&pmid=26158123 #FOAvip Machine learning is continually being applied to a growing set of fields, including the social sciences, business, and medicine. Some fields present problems that are not easily addressed using standard machine learning approaches and, in particular, there is growing interest in differential prediction. In this type of task we are interested in producing a classifier that specifically characterizes a subgroup of interest by maximizing the difference in predictive performance for some outcome between subgroups in a population. We discuss adapting maximum margin classifiers for differential prediction. We first introduce multiple approaches that do not affect the key properties of maximum margin classifiers, but which also do not directly attempt to optimize a standard measure of differential prediction. We next propose a model that directly optimizes a standard measure in this field, the uplift measure. We evaluate our models on real data from two medical applications and show excellent results. Twit Text FOAVip Support Vector Machines for Differential Prediction ????Mach Learn Knowl Discov Databases http://www.kidney.de/pget.php?&tw=1&pmid=26158123 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26158123 #FOAvip English Wikipedia <ref>{{Cite pmid|26158123 }}</ref> Support Vector Machines for Differential Prediction #MMPMID26158123 Kuusisto F ; Santos Costa V ; Nassif H ; Burnside E ; Page D ; Shavlik J Mach Learn Knowl Discov Databases 2014[]; 8725 (?): 50-65 PMID26158123 show ga Machine learning is continually being applied to a growing set of fields, including the social sciences, business, and medicine. Some fields present problems that are not easily addressed using standard machine learning approaches and, in particular, there is growing interest in differential prediction. In this type of task we are interested in producing a classifier that specifically characterizes a subgroup of interest by maximizing the difference in predictive performance for some outcome between subgroups in a population. We discuss adapting maximum margin classifiers for differential prediction. We first introduce multiple approaches that do not affect the key properties of maximum margin classifiers, but which also do not directly attempt to optimize a standard measure of differential prediction. We next propose a model that directly optimizes a standard measure in this field, the uplift measure. We evaluate our models on real data from two medical applications and show excellent results. ?Support Vector Machines for Differential Prediction (epmc).pdf DeepDyve Pubget Overpricing