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Table representation of search results timeline featuring number of search results per year.

Year Number of Results
2005 4
2008 2
2009 11
2010 45
2011 373
2012 680
2013 809
2014 892
2015 882
2016 875
2017 951
2018 1072
2019 586
2020 4
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6,236 results
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Support vector machine with Dirichlet feature mapping.
Nedaie A and Najafi AA. Neural Netw 2018. PMID 29223012
The Support Vector Machine (SVM) is a supervised learning algorithm to analyze data and recognize patterns. The standard SVM suffers from some limitations in nonlinear classification problems. ...
The Support Vector Machine (SVM) is a supervised learning algorithm to analyze data and recognize patterns. The standard SVM s …
Distributed support vector machine in master-slave mode.
Chen Q and Cao F. Neural Netw 2018. PMID 29494875
It is well known that the support vector machine (SVM) is an effective learning algorithm. The alternating direction method of multipliers (ADMM) algorithm has emerged as a powerful technique for solving distributed optimisation models. ...
It is well known that the support vector machine (SVM) is an effective learning algorithm. The alternating direction method of …
Improvements on ν-Twin Support Vector Machine.
Khemchandani R, et al. Neural Netw 2016. PMID 27136663
In this paper, we propose two novel binary classifiers termed as "Improvements on ν-Twin Support Vector Machine: Iν-TWSVM and Iν-TWSVM (Fast)" that are motivated by ν-Twin Support Vector Machine (ν-TWSVM). ...The other properties of Iν-TWSVM, related to support vectors (SVs), are similar to that of ν-TWSVM. To test the efficacy of the proposed method, experiments have been conducted on a wide range of UCI and a skewed variation of NDC datasets. ...
In this paper, we propose two novel binary classifiers termed as "Improvements on ν-Twin Support Vector Machine: Iν-TWSVM and …
Improved multi-view privileged support vector machine.
Tang J, et al. Neural Netw 2018. PMID 30048781
By exploiting the consensus principle or the complementarity principle among different views, various successful support vector machine (SVM)-based multi-view learning models have been proposed for performance improvement. ...
By exploiting the consensus principle or the complementarity principle among different views, various successful support vector
Support vector machine classification trees based on fuzzy entropy of classification.
de Boves Harrington P. Anal Chim Acta 2017. PMID 28081808
The support vector machine (SVM) is a powerful classifier that has recently been implemented in a classification tree (SVMTreeG). ...Also, a kernel version of the fuzzy entropy algorithm was devised. A fast support vector machine implementation is used that has no cost C or slack variables to optimize. ...
The support vector machine (SVM) is a powerful classifier that has recently been implemented in a classification tree (SVMTree …
Ranking Support Vector Machine with Kernel Approximation.
Chen K, et al. Comput Intell Neurosci 2017. PMID 28293256 Free PMC article.
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. ...
Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used. Nonlinear Rank …
Nonparallel support vector regression model and its SMO-type solver.
Tang L, et al. Neural Netw 2018. PMID 29945062
Although the twin support vector regression (TSVR) method has been widely studied and various variants are successfully developed, the structural risk minimization (SRM) principle and model's sparseness are not given sufficient consideration. In this paper, a novel nonparallel support vector regression (NPSVR) is proposed in spirit of nonparallel support vector machine (NPSVM), which outperforms existing twin support vector regression (TSVR) methods in the following terms: (1) For each primal problem, a regularized term is added by rigidly following the SRM principle so that the kernel trick can be applied directly to the dual problems for the nonlinear case without considering an extra kernel-generated surface; (2) An ε-insensitive loss function is adopted to remain inherent sparseness as the standard support vector regression (SVR); (3) The dual problems have the same formulation with that of the standard SVR, so computing inverse matrix is well avoided and a sequential minimization optimization (SMO)-type solver is exclusively designed to accelerate the training for large-scale datasets; (4) The primal problems can approximately degenerate to those of the existing TSVRs if corresponding parameters are appropriately chosen. ...
Although the twin support vector regression (TSVR) method has been widely studied and various variants are successfully develo …
An Efficient Feature Selection Strategy Based on Multiple Support Vector Machine Technology with Gene Expression Data.
Zhang Y, et al. Biomed Res Int 2018. PMID 30228989 Free PMC article.
In order to select determinant genes related to breast cancer from the initial gene expression data, we propose a new feature selection method, namely, support vector machine based on recursive feature elimination and parameter optimization (SVM-RFE-PO). ...Herein, the new feature selection method contains three kinds of algorithms: support vector machine based on recursive feature elimination and grid search (SVM-RFE-GS), support vector machine based on recursive feature elimination and particle swarm optimization (SVM-RFE-PSO), and support vector machine based on recursive feature elimination and genetic algorithm (SVM-RFE-GA). ...
In order to select determinant genes related to breast cancer from the initial gene expression data, we propose a new feature selection meth …
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