lambaste
发表于 2025-3-23 12:09:33
Combining Classifiers Based on Gaussian Mixture Model Approach to Ensemble Datag. In this paper, we focus on combining different classifiers to form an effective ensemble system. By introducing a novel framework operated on outputs of different classifiers, our aim is to build a powerful model which is competitive to other well-known combining algorithms such as Decision Templ
充满人
发表于 2025-3-23 16:22:34
Sentiment Classification of Chinese Reviews in Different Domain: A Comparative Studyws mining plays an important role in the application of product information or public opinion monitoring. Sentiment classification of users’ reviews is one of key issues in the review mining. Comparative study on sentiment classification results of reviews in different domains and the adaptability o
空中
发表于 2025-3-23 20:37:37
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五行打油诗
发表于 2025-3-23 23:04:29
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ovation
发表于 2025-3-24 04:22:32
Classification Based on Lower Integral and Extreme Learning Machinential interaction of a group of attributes. The lower integral is a type of non-linear integral with respect to non-additive set functions, which represents the minimum potential of efficiency for a group of attributes with interaction. Through solving a linear programming problem, the value of lowe
勾引
发表于 2025-3-24 08:10:21
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针叶类的树
发表于 2025-3-24 11:54:55
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SUGAR
发表于 2025-3-24 15:29:27
Comparative Analysis of Density Estimation Based Kernel Regressiontation of a random variable and the non-linear mapping from input to output. There are three commonly used LLKEs, i.e., the Nadaraya-Watson kernel estimator, the Priestley-Chao kernel estimator and the Gasser-Müller kernel estimator. Existing studies show that the performance of LLKE mainly depends
Orchiectomy
发表于 2025-3-24 20:56:14
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飞镖
发表于 2025-3-25 01:37:24
Bandwidth Selection for Nadaraya-Watson Kernel Estimator Using Cross-Validation Based on Different P generalized cross-validation (.), the Shibata’s model selector (.), the Akaike’s information criterion (.) and the Akaike’s finite prediction error (.)) are introduced to relieve the problem of selecting over-smoothing bandwidth parameter by the traditional cross-validation for kernel regression pr