争论
发表于 2025-3-25 05:48:14
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dominant
发表于 2025-3-25 10:57:09
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出价
发表于 2025-3-25 14:54:50
Christina Namugenyi,Shastri L. Nimmagadda,Neel Mani
agglomerate
发表于 2025-3-25 16:41:15
Robert Brehm,Mareike Redder,Gordon Flaegel,Jendrik Menz,Cecil Bruce-Boye
irradicable
发表于 2025-3-25 21:41:44
Designing Interactive Topic Discovery Systems for Research and Decision Making,op systems that support human in the loop interaction and refinement of topic models. This paper presents design guidelines for developing software that is able to interactively support researchers and decision makers in the discovery and interpretation of topics found in both small and large domain
单调性
发表于 2025-3-26 01:39:23
Daily Stress Recognition System Using Activity Tracker and Smartphone Based on Physical Activity anal issues. The emerging technology, such as wearable sensors and smartphone, have enabled the opportunity of using the technology to help solve the issue. In this paper, we proposed a system using Internet of Things architecture where we adopted an activity tracker as our sensing device to reduce cu
条街道往前推
发表于 2025-3-26 05:07:15
Listwise Recommendation Approach with Non-negative Matrix Factorization,days many studies reveal that the ultimate goal of recommendations is to predict correct rankings of these unrated items. However, most of the pioneering efforts on . MF predict users’ item ranking based on the original rating matrix, which fails to explicitly present users’ preference ranking on it
Harbor
发表于 2025-3-26 12:16:48
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疲惫的老马
发表于 2025-3-26 15:11:35
Multivariate Multiple Orthogonal Linear Regression,t variables and independent variables. It is possible to use the MMOLR as a step to be followed by multivariate linear regression to compare and investigate the relationships between dependent variables, which are still limited in the multivariate linear regression model. The MMOLR takes into accoun
Brain-Waves
发表于 2025-3-26 20:24:01
Supervised Multiclass Classifier for Autocoding Based on Partition Coefficient,ous study, the supervised multiclass classifier was developed for autocoding, which has the advantages of simplicity and practical calculation time. However, the previous algorithm classified a few objects incorrectly. To address this problem, a new supervised multiclass classifier is proposed that