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Autor: Song, Minghui, Zou, Guohua, Wan, Alan T. K.

Model averaging has gained significant attention in recent years due to its ability of fusing information from different models. The critical challenge in frequentist model averaging is the choice of weight vector. The bootstrap method, known for its

Externí odkaz: http://arxiv.org/abs/2412.05687

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Expectations Versus Reality: Evaluating Intrusion Detection Systems in Practice

Autor: Hesford, Jake, Cheng, Daniel, Wan, Alan, Huynh, Larry, Kim, Seungho, Kim, Hyoungshick, Hong, Jin B.

Our paper provides empirical comparisons between recent IDSs to provide an objective comparison between them to help users choose the most appropriate solution based on their requirements. Our results show that no one solution is the best, but is dep

Externí odkaz: http://arxiv.org/abs/2403.17458

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Model Averaging for Support Vector Machine by Cross-Validation

Autor: Zou, Jiahui, Yuan, Chaoxia, Zhang, Xinyu, Zou, Guohua, Wan, Alan T. K.

Support vector machine (SVM) is a well-known statistical technique for classification problems in machine learning and other fields. An important question for SVM is the selection of covariates (or features) for the model. Many studies have considere

Externí odkaz: http://arxiv.org/abs/2112.14578

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On the asymptotic distribution of model averaging based on information criterion

Autor: Wang, Miaomiao, Zhang, Xinyu, Wan, Alan T. K., Zou, Guohua

Smoothed AIC (S-AIC) and Smoothed BIC (S-BIC) are very widely used in model averaging and are very easily to implement. Especially, the optimal model averaging method MMA and JMA have only been well developed in linear models. Only by modifying, they

Externí odkaz: http://arxiv.org/abs/1910.12208

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