標(biāo)題: Titlebook: Event Attendance Prediction in Social Networks; Xiaomei Zhang,Guohong Cao Book 2021 The Author(s), under exclusive license to Springer Nat [打印本頁] 作者: DEIFY 時(shí)間: 2025-3-21 17:00
書目名稱Event Attendance Prediction in Social Networks影響因子(影響力)
書目名稱Event Attendance Prediction in Social Networks影響因子(影響力)學(xué)科排名
書目名稱Event Attendance Prediction in Social Networks網(wǎng)絡(luò)公開度
書目名稱Event Attendance Prediction in Social Networks網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Event Attendance Prediction in Social Networks被引頻次
書目名稱Event Attendance Prediction in Social Networks被引頻次學(xué)科排名
書目名稱Event Attendance Prediction in Social Networks年度引用
書目名稱Event Attendance Prediction in Social Networks年度引用學(xué)科排名
書目名稱Event Attendance Prediction in Social Networks讀者反饋
書目名稱Event Attendance Prediction in Social Networks讀者反饋學(xué)科排名
作者: 不能約 時(shí)間: 2025-3-21 21:14
Related Work,tion, the initial discussion centers around this topic. Existing works on short-term mobility prediction and long-term mobility prediction are reviewed. Then, we survey related work on event-based social networks, with focuses on recommendation systems and event attendance prediction.作者: Coeval 時(shí)間: 2025-3-22 01:18 作者: 建筑師 時(shí)間: 2025-3-22 07:26
Event Attendance Prediction: Attributes,ds to predict the event attendance. This chapter focuses on identifying the context-aware attributes. The definition of context-aware attributes requires analysis of past events with similar topics. Therefore, we first present a semantic analysis method to calculate the semantic similarity between e作者: TATE 時(shí)間: 2025-3-22 11:54 作者: SPURN 時(shí)間: 2025-3-22 13:36
Performance Evaluations,of the proposed solutions and evaluate how different parameters affect the performances. In this chapter, we first discuss the data selection, the experiment setting, and then present the evaluation results on the effectiveness of individual attributes and the performance of the three classifiers.作者: SPURN 時(shí)間: 2025-3-22 19:26 作者: CARE 時(shí)間: 2025-3-23 00:40
978-3-030-89261-6The Author(s), under exclusive license to Springer Nature Switzerland AG 2021作者: 衍生 時(shí)間: 2025-3-23 03:55 作者: RECUR 時(shí)間: 2025-3-23 07:41 作者: 路標(biāo) 時(shí)間: 2025-3-23 12:17 作者: Soliloquy 時(shí)間: 2025-3-23 15:19
SpringerBriefs in Statisticshttp://image.papertrans.cn/e/image/317407.jpg作者: 憤世嫉俗者 時(shí)間: 2025-3-23 20:06
https://doi.org/10.1007/978-3-319-00783-0attendance, which has three research challenges, i.e., dataset collection, extraction of appropriate attributes, and identifying suitable learning methods. We first explain these challenges and then describe how to address them with a context-aware data mining approach. In this approach, three sets 作者: 翻布尋找 時(shí)間: 2025-3-24 00:13
Astrophysics and Space Science Librarytion, the initial discussion centers around this topic. Existing works on short-term mobility prediction and long-term mobility prediction are reviewed. Then, we survey related work on event-based social networks, with focuses on recommendation systems and event attendance prediction.作者: HEW 時(shí)間: 2025-3-24 05:54 作者: 卵石 時(shí)間: 2025-3-24 06:58 作者: labile 時(shí)間: 2025-3-24 14:04
https://doi.org/10.1007/978-3-7091-0900-7e. This process is also referred to as supervised binary classification, considering that ‘a(chǎn)ttend or not’ is a binary classification. There are many supervised classifiers in the literature, and we adopt three classifiers, including logistic regression, decision tree and na?ve Bayes. In this chapter作者: hemoglobin 時(shí)間: 2025-3-24 16:25
Group IV materials (mainly SiC),of the proposed solutions and evaluate how different parameters affect the performances. In this chapter, we first discuss the data selection, the experiment setting, and then present the evaluation results on the effectiveness of individual attributes and the performance of the three classifiers.作者: 領(lǐng)導(dǎo)權(quán) 時(shí)間: 2025-3-24 22:49
https://doi.org/10.1007/0-306-46940-5ng approach to solve it. Experimental results based on the collected dataset demonstrated that the proposed approach can predict event attendance with high accuracy. Finally, we point out future research directions.作者: 調(diào)整校對 時(shí)間: 2025-3-25 01:42
Astrophysics and Space Science Librarytion, the initial discussion centers around this topic. Existing works on short-term mobility prediction and long-term mobility prediction are reviewed. Then, we survey related work on event-based social networks, with focuses on recommendation systems and event attendance prediction.作者: Mitigate 時(shí)間: 2025-3-25 07:16 作者: irreducible 時(shí)間: 2025-3-25 08:24
Group IV materials (mainly SiC),of the proposed solutions and evaluate how different parameters affect the performances. In this chapter, we first discuss the data selection, the experiment setting, and then present the evaluation results on the effectiveness of individual attributes and the performance of the three classifiers.作者: MORPH 時(shí)間: 2025-3-25 13:14
https://doi.org/10.1007/0-306-46940-5ng approach to solve it. Experimental results based on the collected dataset demonstrated that the proposed approach can predict event attendance with high accuracy. Finally, we point out future research directions.作者: 潰爛 時(shí)間: 2025-3-25 17:17 作者: 充滿裝飾 時(shí)間: 2025-3-25 21:39 作者: 闡釋 時(shí)間: 2025-3-26 03:38 作者: Duodenitis 時(shí)間: 2025-3-26 05:22 作者: 功多汁水 時(shí)間: 2025-3-26 10:07 作者: PHIL 時(shí)間: 2025-3-26 15:25 作者: VEST 時(shí)間: 2025-3-26 17:02
Event Attendance Prediction: Attributes,res analysis of past events with similar topics. Therefore, we first present a semantic analysis method to calculate the semantic similarity between events, and then explain the three sets of context-aware attributes in details, i.e., semantic attributes, temporal attributes, and spatial attributes.作者: 初學(xué)者 時(shí)間: 2025-3-26 23:56
Event Attendance Prediction: Learning Methods,upervised classifiers in the literature, and we adopt three classifiers, including logistic regression, decision tree and na?ve Bayes. In this chapter, we first give an overview of the data mining process, and then present the details of these supervised classifiers.作者: 小卷發(fā) 時(shí)間: 2025-3-27 03:49 作者: Little 時(shí)間: 2025-3-27 07:32 作者: 信條 時(shí)間: 2025-3-27 09:54 作者: Debility 時(shí)間: 2025-3-27 16:07 作者: 陶醉 時(shí)間: 2025-3-27 19:01
Book 2021identified by analyzing users’ past activities, including semantic, temporal, and spatial attributes. This book illustrates how these attributes can be applied for event attendance prediction by incorporating them into supervised learning models, and demonstrates their effectiveness through a real-w作者: Synthesize 時(shí)間: 2025-3-28 01:26
2191-544X attributes. This book illustrates how these attributes can be applied for event attendance prediction by incorporating them into supervised learning models, and demonstrates their effectiveness through a real-w978-3-030-89261-6978-3-030-89262-3Series ISSN 2191-544X Series E-ISSN 2191-5458 作者: aggrieve 時(shí)間: 2025-3-28 02:07
,Bin?re Codierung,is proposed in this study. However, simulations show that with targeting about one quarter of poor children would be erroneously excluded (under-coverage), while more than a third of non-poor children would be erroneously included (leakage). These identification errors, which increase in proportion 作者: Nebulous 時(shí)間: 2025-3-28 08:52 作者: FIR 時(shí)間: 2025-3-28 13:41 作者: 轎車 時(shí)間: 2025-3-28 17:34
https://doi.org/10.1007/978-3-662-09734-2Arbeitsschutz; Biokontamination; Chemische Verfahrenstechnik; Entwicklung; Filtration; Hygiene; Laminar Fl作者: Bereavement 時(shí)間: 2025-3-28 21:28
