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• W4313564557 abstract "In this work, we propose two novel feature mapping techniques based on multivariate Beta distribution and its mixture models. Support vector machine (SVM) is one of the most famous and powerful discriminative classifiers which has been applied in various domains. We improved its discrimination power by considering the nature of data and integrating discriminative approach with generative method. Such a novel hybrid method could improve the accuracy of the model compared to SVM with traditional kernels. To evaluate our model performance, we applied it to medical applications including Barrett’s oesophagus detection and colonoscopy image analysis. The outputs indicate that our proposed model could be considered as a promising alternative." @default.
• W4313564557 created "2023-01-06" @default.
• W4313564557 creator A5090600716 @default.
• W4313564557 creator A5091833816 @default.
• W4313564557 date "2022-08-22" @default.
• W4313564557 modified "2023-09-24" @default.
• W4313564557 title "A Flexible and Hybrid Feature Mapping Technique Based on multivariate Beta Distribution Kernel Applied to Medical Applications" @default.
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• W4313564557 doi "https://doi.org/10.1109/icit48603.2022.10002760" @default.
• W4313564557 hasPublicationYear "2022" @default.
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