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10.1155/2015/878291 http://scihub22266oqcxt.onion/10.1155/2015/878291 C4386713!4386713!25879039     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biomed+Res+Int 2015 ; 2015 (ä): ä Nephropedia Template TP {{tp|p=25879039|t=2015. A Linear-RBF Multikernel SVM to Classify Big Text Corpora |pdf=|usr=}}{{25879039}} gab.com Text A Linear-RBF Multikernel SVM to Classify Big Text Corpora JO: Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=25879039 #FOAvip Support vector machine (SVM) is a powerful technique for classification. However, SVM is not suitable for classification of large datasets or text corpora, because the training complexity of SVMs is highly dependent on the input size. Recent developments in the literature on the SVM and other kernel methods emphasize the need to consider multiple kernels or parameterizations of kernels because they provide greater flexibility. This paper shows a multikernel SVM to manage highly dimensional data, providing an automatic parameterization with low computational cost and improving results against SVMs parameterized under a brute-force search. The model consists in spreading the dataset into cohesive term slices (clusters) to construct a defined structure (multikernel). The new approach is tested on different text corpora. Experimental results show that the new classifier has good accuracy compared with the classic SVM, while the training is significantly faster than several other SVM classifiers. Twit Text FOAVip A Linear-RBF Multikernel SVM to Classify Big Text Corpora ????Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=25879039 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25879039 #FOAvip English Wikipedia <ref>{{Cite pmid|25879039}}</ref> A Linear-RBF Multikernel SVM to Classify Big Text Corpora #MMPMID25879039 Romero R; Iglesias EL; Borrajo L Biomed Res Int 2015[]; 2015 (ä): ä PMID25879039show ga Support vector machine (SVM) is a powerful technique for classification. However, SVM is not suitable for classification of large datasets or text corpora, because the training complexity of SVMs is highly dependent on the input size. Recent developments in the literature on the SVM and other kernel methods emphasize the need to consider multiple kernels or parameterizations of kernels because they provide greater flexibility. This paper shows a multikernel SVM to manage highly dimensional data, providing an automatic parameterization with low computational cost and improving results against SVMs parameterized under a brute-force search. The model consists in spreading the dataset into cohesive term slices (clusters) to construct a defined structure (multikernel). The new approach is tested on different text corpora. Experimental results show that the new classifier has good accuracy compared with the classic SVM, while the training is significantly faster than several other SVM classifiers. äA Linear-RBF Multikernel SVM to Classify Big Text Corpora (epmc).pdf DeepDyve Pubget Overpricing