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https://ieeexplore.ieee.org/document/1528532/
Abstract: This paper proposes a complete framework of posterior probability support vector machines (PPSVMs) for weighted training samples using modified concepts of risks, linear separability, margin, and optimal hyperplane. Within this framework, a new optimization problem for unbalanced classification problems is formulated and a new concept of support vectors established.Cited by: 132
http://sourcedb.ict.cas.cn/cn/ictthesis/200907/P020090722595513469407.pdf
IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 16, NO. 6, NOVEMBER 2005 1561 Posterior Probability Support Vector Machines for Unbalanced Data Qing Tao, Gao-Wei Wu, Fei-Yue Wang, Fellow, IEEE, and Jue Wang, Senior Member, IEEE Abstract—This paper proposes a complete framework of poste- rior probability support vector machines (PPSVMs) for weightedCited by: 132
https://www.researchgate.net/publication/7426558_Posterior_Probability_Support_Vector_Machines_for_Unbalanced_Data_Neural_Networks
This paper proposes a complete framework of posterior probability support vector machines (PPSVMs) for weighted training samples using modified concepts of risks, linear separability, margin, and ...
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.452.1728
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Abstract—This paper proposes a complete framework of poste-rior probability support vector machines (PPSVMs) for weighted training samples using modified concepts of risks, linear separa-bility, margin, and optimal hyperplane. Within this framework, a new optimization problem for unbalanced classification …
https://www.mathworks.com/help/stats/fitsvmposterior.html
ScoreSVMModel = fitSVMPosterior(SVMModel) returns ScoreSVMModel, which is a trained, support vector machine (SVM) classifier containing the optimal score-to-posterior-probability transformation function for two-class learning.. The software fits the appropriate score-to-posterior-probability transformation function using the SVM classifier SVMModel, and by cross validation using the stored ...
https://in.mathworks.com/help/stats/classificationsvm.fitposterior.html
ScoreSVMModel = fitPosterior(SVMModel) returns a trained support vector machine (SVM) classifier ScoreSVMModel containing the optimal score-to-posterior-probability transformation function for two-class learning. For more details, see Algorithms.
https://www.sciencedirect.com/science/article/pii/S0167923612000425
Highlights Support vector machine (SVM) acts as a preprocessor for unbalanced data. SVM generates extra data related to minority class. The modified training data is used to train multiple classification techniques. The hybrid approach performs well in terms of sensitivity.Cited by: 86
https://www.researchgate.net/publication/5582029_Multiclass_Posterior_Probability_Support_Vector_Machines
Multiclass Posterior Probability Support Vector Machines Article (PDF Available) in IEEE Transactions on Neural Networks 19(1):130-9 · February 2008 with 131 Reads How we measure 'reads'
http://cseweb.ucsd.edu/~elkan/254spring01/jdrishrep.pdf
Obtaining Calibrated Probability Estimates from Support Vector Machines Joseph Drish ... training on unbalanced data sets to find the best parameters for the SVM classifiers. We demonstrate that using the F1 value as a metric for tun- ... conditional posterior probability, or P(jjx).
https://core.ac.uk/display/99970110
Compared with fuzzy support vector machines (FSVMs), the pro-posed PPSVM is a natural and an analytical extension of regular SVMs based on the statistical learning theory. Index Terms—Bayesian decision theory, classification, margin, maximal margin algorithms,-SVM, posterior probability, sup
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