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https://www.sciencedirect.com/science/article/pii/S0031320303001754
1. Introduction. The support vector machine (SVM) is a new and promising classification and regression technique proposed by Vapnik and his group at AT&T Bell Laboratories .The SVM learns a separating hyperplane to maximize the margin and to produce a good generalization ability .Recent theoretical research work has solved the existing difficulties of using the SVM in practical applications , .Cited by: 537
https://www.sciencedirect.com/science/article/abs/pii/S0031320303001754
Even the support vector machine (SVM) has been proposed to provide a good generalization performance, the classification result of the practically implemented SVM is often far from the theoretically expected level because their implementations are based on the approximated algorithms due to the high complexity of time and space.Cited by: 537
https://www.researchgate.net/publication/222707017_Constructing_support_vector_machine_ensemble
Constructing support vector machine ensemble. ... Such inconsistent data are usually treated as noise and removed from the original dataset before conducting analyses or constructing prediction ...
https://link.springer.com/article/10.1007/s00521-012-1041-z
Jul 10, 2012 · A novel method, namely ensemble support vector machine with segmentation (SeEn–SVM), for the classification of imbalanced datasets is proposed in this paper. In particular, vector quantization algorithm is used to segment the majority class and hence generates some small datasets that are of less imbalance than original one, and two different weighted functions are proposed to …Cited by: 12
http://ebiotrade.com/emagazine/content/5/2005_12_3_4/A5A6FE72-A20D-406A-A72B-2E9F63280BD0/pdf/GPB%203(4)-08.pdf
Article Constructing Support Vector Machine Ensembles for Cancer Classification Based on Proteomic Profiling Yong Mao1*, Xiao-Bo Zhou2, Dao-Ying Pi1, and You-Xian Sun1 1National Laboratory of Industrial Control Technology, Institute of Modern Control Engineering, Zhejiang University, Hangzhou 310027, China; 2Harvard Center for Neurodegeneration and Repair, Harvard MedicalCited by: 5
http://jmlr.csail.mit.edu/papers/volume9/lin08a/lin08a.pdf
constructing such an ensemble is a challenging task (Vapnik, 1998). In this paper, we conquer the task of infinite ensemble learning, and demonstrate that better performance can be achieved by going from finite ensembles to infinite ones. We formulate a framework for infinite ensemble learning based on the support vector machine (SVM ...
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.100.7589
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Even the support vector machine (SVM) has been proved to improve the classification performance greatly than a single SVM, the classification result of the practically implemented SVM is often far from the theoretically expected level because they don’t evaluate the importance degree of the output of individual ...
http://work.caltech.edu/~htlin/publication/doc/infkernel.pdf
Keywords: ensemble learning, boosting, support vector machine, kernel 1. Introduction Ensemble learning algorithms, such as boosting (Freund and Schapire, 1996), are successful in practice (Meir and R¨atsch, 2003). They construct a classifier that averages over some base hypotheses in a set H.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.67.7233
It is not clear whether we should construct an ensemble classifier with a larger or even infinite number of hypotheses. In addition, constructing an infinite ensemble itself is a challenging task. In this paper, we formulate an infinite ensemble learning framework based on SVM.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5217832/
Jan 06, 2017 · Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques.Cited by: 54
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