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Titlebook: Advances in Knowledge Discovery and Data Mining; 18th Pacific-Asia Co Vincent S. Tseng,Tu Bao Ho,Hung-Yu Kao Conference proceedings 2014 Sp

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發(fā)表于 2025-3-21 16:17:29 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Advances in Knowledge Discovery and Data Mining
期刊簡(jiǎn)稱18th Pacific-Asia Co
影響因子2023Vincent S. Tseng,Tu Bao Ho,Hung-Yu Kao
視頻videohttp://file.papertrans.cn/149/148625/148625.mp4
學(xué)科分類Lecture Notes in Computer Science
圖書封面Titlebook: Advances in Knowledge Discovery and Data Mining; 18th Pacific-Asia Co Vincent S. Tseng,Tu Bao Ho,Hung-Yu Kao Conference proceedings 2014 Sp
影響因子The two-volume set LNAI 8443 + LNAI 8444 constitutes the refereed proceedings of the 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014, held in Tainan, Taiwan, in May 2014. The 40 full papers and the 60 short papers presented within these proceedings were carefully reviewed and selected from 371 submissions. They cover the general fields of pattern mining; social network and social media; classification; graph and network mining; applications; privacy preserving; recommendation; feature selection and reduction; machine learning; temporal and spatial data; novel algorithms; clustering; biomedical data mining; stream mining; outlier and anomaly detection; multi-sources mining; and unstructured data and text mining.
Pindex Conference proceedings 2014
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Persistent Community Detection in Dynamic Social Networkst, wie sie bei uns vielfach schon vorhanden sind, für s?mtliche wirtschaftsberatenden Berufe notwendig sind, oder ob sie sich gegebenenfalls als Hemmschuh für diese Gruppen erweisen. Mu?, beziehungsweise soll grunds?tzlich allen freien Berufen im Rahmen unseres Wettbewerbsrechts eine Sonderstellung
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Learning from Crowds under Experts’ Supervision sondere in der Art ausüben, da? es noch Aspekte oder Ans?tze geben mag, unter denen sie bisher noch nicht behandelt wurden und die neuere, bessere oder tiefere oder auch nur andere Einsichten erlauben. Ein solches Thema ist das zwischen dem Ersten und dem Zweiten im Ablauf, zwischen Pionier und Nac
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Franz Gerstenbrand,Werner Poewe,Gerald Sternascades caused by a noteworthy event. The results of extensive empirical experiments on real-life big social networks data show that our algorithm performs much better than a set of baseline methods, including two alternative models and the state-of-the-art.
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https://doi.org/10.1007/b138742ce of crowdsourcing learning tasks with some additional expert labels by treating each labeler as a personal classifier and combining all labelers’ opinions from a model combination perspective. Experiments show that our method can significantly improve the learning quality as compared with those methods solely using crowd labels.
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