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10.1155/2017/4629534 http://scihub22266oqcxt.onion/10.1155/2017/4629534 C5331172!5331172 !28293256     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 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\28293256 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Comput+Intell+Neurosci 2017 ; 2017 (ä): 4629534 Nephropedia Template TP {{tp|p=28293256 |t=2017. Ranking Support Vector Machine with Kernel Approximation |pdf=|usr=}}{{28293256 }} gab.com Text Ranking Support Vector Machine with Kernel Approximation JO: Comput Intell Neurosci http://www.kidney.de/pget.php?&tw=1&pmid=28293256 #FOAvip Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels) can give higher accuracy than linear RankSVM (RankSVM with a linear kernel) for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss) objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms. Twit Text FOAVip Ranking Support Vector Machine with Kernel Approximation ????Comput Intell Neurosci http://www.kidney.de/pget.php?&tw=1&pmid=28293256 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28293256 #FOAvip English Wikipedia <ref>{{Cite pmid|28293256 }}</ref> Ranking Support Vector Machine with Kernel Approximation #MMPMID28293256 Chen K ; Li R ; Dou Y ; Liang Z ; Lv Q Comput Intell Neurosci 2017[]; 2017 (ä): 4629534 PMID28293256 show ga Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels) can give higher accuracy than linear RankSVM (RankSVM with a linear kernel) for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss) objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms. |*Artificial Intelligence [MESH] |*Models, Theoretical [MESH] |*Support Vector Machine [MESH]Ranking Support Vector Machine with Kernel Approximation (epmc).pdf DeepDyve Pubget Overpricing