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10.1109/TSMCB.2008.2011645 http://scihub22266oqcxt.onion/10.1109/TSMCB.2008.2011645 C4467789!4467789 !19336328     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\19336328 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       IEEE+Trans+Syst+Man+Cybern+B+Cybern 2009 ; 39 (4 ): 989-1001 Nephropedia Template TP {{tp|p=19336328 |t=2009. Fast support vector machines for continuous data |pdf=|usr=}}{{19336328 }} gab.com Text Fast support vector machines for continuous data JO: IEEE Trans Syst Man Cybern B Cybern http://www.kidney.de/pget.php?&tw=1&pmid=19336328 #FOAvip Support vector machines (SVMs) can be trained to be very accurate classifiers and have been used in many applications. However, the training time and, to a lesser extent, prediction time of SVMs on very large data sets can be very long. This paper presents a fast compression method to scale up SVMs to large data sets. A simple bit-reduction method is applied to reduce the cardinality of the data by weighting representative examples. We then develop SVMs trained on the weighted data. Experiments indicate that bit-reduction SVM produces a significant reduction in the time required for both training and prediction with minimum loss in accuracy. It is also shown to typically be more accurate than random sampling when the data are not overcompressed. Twit Text FOAVip Fast support vector machines for continuous data ????IEEE Trans Syst Man Cybern B Cybern http://www.kidney.de/pget.php?&tw=1&pmid=19336328 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=19336328 #FOAvip English Wikipedia <ref>{{Cite pmid|19336328 }}</ref> Fast support vector machines for continuous data #MMPMID19336328 Kramer KA ; Hall LO ; Goldgof DB ; Remsen A ; Luo T IEEE Trans Syst Man Cybern B Cybern 2009[Aug]; 39 (4 ): 989-1001 PMID19336328 show ga Support vector machines (SVMs) can be trained to be very accurate classifiers and have been used in many applications. However, the training time and, to a lesser extent, prediction time of SVMs on very large data sets can be very long. This paper presents a fast compression method to scale up SVMs to large data sets. A simple bit-reduction method is applied to reduce the cardinality of the data by weighting representative examples. We then develop SVMs trained on the weighted data. Experiments indicate that bit-reduction SVM produces a significant reduction in the time required for both training and prediction with minimum loss in accuracy. It is also shown to typically be more accurate than random sampling when the data are not overcompressed. äFast support vector machines for continuous data (epmc).pdf DeepDyve Pubget Overpricing