Find all needed information about Weighted Margin Support Vector Machine. Below you can see links where you can find everything you want to know about Weighted Margin Support Vector Machine.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4960100/
In this paper, I propose the weighted K-means support vector machine (wKM-SVM) and weighted support vector machine (wSVM), for which I allow the SVM to impose weights to the loss term. Besides, I demonstrate the numerical relations between the objective function of the SVM and weights.Cited by: 9
https://datascienceplus.com/weighted-linear-support-vector-machine/
Apr 06, 2017 · Notice that the proportion of spam and ham in the training data set is similar to that of the entire data. One of the widely used classifiers is Linear Support Vector Machine. From my last writing on Linear Support Vector Machine, you can find that in case of Linear SVM we solve the following optimization problem.Author: Ananda Das
https://arxiv.org/pdf/1310.3003.pdf
method is named Distance-weighted Support Vector Machine (DWSVM) to salute the above two classical methods. As shown in Panel (d) of Figure1, DWSVM preserves a good direction by showing the Gaussian pattern in the projections while nds a good intercept term which is …
https://www.iis.sinica.edu.tw/page/jise/2014/201411_06.pdf
Least squares twin support vector machine (LS-TSVM) aims at resolving a pair of smaller-sized quadratic programming problems (QPPs) instead of a single large one as in the conventional least squares support vector machine (LS-SVM), which makes the learning speed of LS-TSVM faster than that of LS-SVM. However, same penalties are
https://www.researchgate.net/publication/221653619_Incorporating_prior_knowledge_with_weighted_margin_support_vector_machines
Weighted Margin Support Vector Machines (WMSVM) [49] is another way to solve the small-sampling problem by generalizing the original Support Vector Machines for incorporating prior knowledge. The ...
https://www.mathworks.com/help/stats/classificationsvm.resubmargin.html
m = resubMargin(SVMModel) returns the resubstitution classification margins (m) for the support vector machine (SVM) classifier SVMModel using the training data stored in SVMModel.X and the corresponding class labels stored in SVMModel.Y.. m is returned as a numeric vector …
https://www.sciencedirect.com/science/article/pii/S0895717710005315
Learning SVM with weighted maximum margin criterion for classification of imbalanced data. ... whether the selected kernel matches the data determines the performance of support vector machine. Conventional support vector classifiers are not suitable to the imbalanced learning tasks since they tend to classify the instances to the majority ...Cited by: 22
https://link.springer.com/article/10.1007%2Fs11063-004-1640-5
Nov 01, 2004 · The ideas from fuzzy neural networks and support vector machine (SVM) are incorporated to make SVM classifiers perform better. The influence of the samples with high uncertainty can be decreased by employing the fuzzy membership to weigh the margin of each training vector. The linear separability, fuzzy margin, optimal hyperplane, generalization and soft fuzzy margin algorithms …Cited by: 26
https://www.researchgate.net/publication/4202393_Weighted_support_vector_machine_for_data_classification
This paper presents a weighted support vector machine (WSVM) to improve the outlier sensitivity problem of standard support vector machine (SVM) for two-class data classification.
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