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- W3015498506 abstract "Support Vector Machine is one of the most popular, promising and well-known machine learning tools for classification, regression and novelty detection. The performance of SVM totally depends on its core component of kernel functions like linear, polynomial and radial basis function (RBF). Conventional kernel functions have advantages and drawbacks so it is very difficult to give an excellent performance while using only one kernel function for the large nonlinear dataset to nonlinear mapping from original input space to a high dimensional space. For this reason, in this paper, we proposed a hybrid kernel function combining polynomial and RBF kernel function and data preprocessing algorithms to reduce noisy data for increasing learning ability and generalization performance. These two factors jointly improve the performance of SVM. To validate our proposed model, we used two different nonlinear breast cancer and heart disease datasets with different characteristics. The proposed method achieved the accuracies of 98.55%, 99.47% and 98.36% along with the sensitivity of 100% in the testing phase of these three datasets respectively. The simulation result shows that its performance is better than that of other SVMs constructed by ordinary kernel functions and existing methods." @default.
- W3015498506 created "2020-04-17" @default.
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- W3015498506 date "2019-11-01" @default.
- W3015498506 modified "2023-10-11" @default.
- W3015498506 title "A Modified Support Vector Machine with Hybrid Kernel Function for Diagnosis of Diseases" @default.
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- W3015498506 doi "https://doi.org/10.1109/becithcon48839.2019.9063176" @default.
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