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10.1007/s10618-020-00692-x http://scihub22266oqcxt.onion/10.1007/s10618-020-00692-x C7330274!7330274!32837252     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       Data+Min+Knowl+Discov 2020 ; 34 (6): 1676-712 Nephropedia Template TP {{tp|p=32837252|t=2020. Gaussian bandwidth selection for manifold learning and classification |pdf=|usr=}}{{32837252}} gab.com Text Gaussian bandwidth selection for manifold learning and classification JO: Data Min Knowl Discov http://www.kidney.de/pget.php?&tw=1&pmid=32837252 #FOAvip Kernel methods play a critical role in many machine learning algorithms. They are useful in manifold learning, classification, clustering and other data analysis tasks. Setting the kernel?s scale parameter, also referred to as the kernel?s bandwidth, highly affects the performance of the task in hand. We propose to set a scale parameter that is tailored to one of two types of tasks: classification and manifold learning. For manifold learning, we seek a scale which is best at capturing the manifold?s intrinsic dimension. For classification, we propose three methods for estimating the scale, which optimize the classification results in different senses. The proposed frameworks are simulated on artificial and on real datasets. The results show a high correlation between optimal classification rates and the estimated scales. Finally, we demonstrate the approach on a seismic event classification task. Twit Text FOAVip Gaussian bandwidth selection for manifold learning and classification ????Data Min Knowl Discov http://www.kidney.de/pget.php?&tw=1&pmid=32837252 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32837252 #FOAvip English Wikipedia <ref>{{Cite pmid|32837252}}</ref> Gaussian bandwidth selection for manifold learning and classification #MMPMID32837252 Lindenbaum O; Salhov M; Yeredor A; Averbuch A Data Min Knowl Discov 2020[]; 34 (6): 1676-712 PMID32837252show ga Kernel methods play a critical role in many machine learning algorithms. They are useful in manifold learning, classification, clustering and other data analysis tasks. Setting the kernel?s scale parameter, also referred to as the kernel?s bandwidth, highly affects the performance of the task in hand. We propose to set a scale parameter that is tailored to one of two types of tasks: classification and manifold learning. For manifold learning, we seek a scale which is best at capturing the manifold?s intrinsic dimension. For classification, we propose three methods for estimating the scale, which optimize the classification results in different senses. The proposed frameworks are simulated on artificial and on real datasets. The results show a high correlation between optimal classification rates and the estimated scales. Finally, we demonstrate the approach on a seismic event classification task. äGaussian bandwidth selection for manifold learning and classification (epmc).pdf DeepDyve Pubget Overpricing