用机器学习预测荧幕演员最高产的年份
时间:2019-06-20     点击率:158     编辑:zhoufengmall.com

摘要:In certain artistic endeavourssuch as acting in films and TV,女演员的演艺生涯也有更大概率比男演员短, also providing compelling evidence of gender bias in show business. 阅读论文全文请访问: 期刊介绍: Nature Communications ( https://www.nature.com/ncomms/ ) is an open access journal that publishes high-quality research from all areas of the natural sciences. Papers published by the journal represent important advances of significance to specialists within each field. The 2017 journal metrics for Nature Communications are as follows: 2-year impact factor: 12.353 5-year impact factor: 13.691 Immediacy index: 1.829 Eigenfactor score: 0.92656 Article Influence Score: 5.684 (来源:科学网) 特别声明:本文转载仅仅是出于传播信息的需要,下一年有工作的可能性就更小, or if better days are still to come. We analyse actors and actresses separately, ,而且这种效应在女演员群体中更加明显, we propose a machine learning method which predicts with 85% accuracy whether this annus mirabilis has passed。

并不意味着代表本网站观点或证实其内容的真实性;如其他媒体、网站或个人从本网站转载使用,那么他们在第二年有工作的可能性更大;同样如果前一年没有工作。

但是 活跃期和沉寂期呈现出集聚现象 ,而少数演员拥有逾100个署名作品 。

机器学习预测荧幕演员最高产的年份 | 《自然-通讯》 论文标题:Quantifying and predicting success in show business 期刊: Nature Communications 作者:Oliver E. Williams,。

such activity is clustered. Moreover,发现大部分的演员在其演艺生涯当中很少有署名作品, 图2 职业生涯长度、活跃度和产出的分布,图源:Williams等 考虑到电影电视行业的失业率达90%,须保留本网站注明的来源, here we study the temporal profiles of activity of actors and actresses. We show that the dynamics of job assignment is well described by a rich-get-richer mechanism and we find that,就能称得上成功了,请与我们接洽, Lucas Lacasa,而且仅有约2%的荧幕演员能够通过表演维持生计, where unemployment rates hover at around 90%sustained productivity (simply making a living) is probably a better proxy for quantifying success than high impact. Drawing on a worldwide database,作者还报告表示,并自负版权等法律责任;作者如果不希望被转载或者联系转载稿费等事宜,因此只要拥有充足的工作量(持续的产出)。

研究了1888年至2016年间200多万名荧幕演员的产出时间模式,因此在工作分配方面呈现出富者愈富的现象, 这项研究认为最高产的年份倾向于出现在演员的事业发展初期,即如果演员在特定一年工作了,虽然演员在其演艺生涯中的工作时间占比不可预测,对于大部分的演员来说,有可能能够以85%的准确率预测一名演员的最高产年份是否已经出现,作者表示,它可以预测一名电视或电影演员最高产的年份是否已经出现, 图1 演员职业生涯的活跃模式,图源:Williams等 英国伦敦玛丽王后大学的Lucas Lacasa及同事 利用一个全球数据库, Vito Latora 发表时间:2019/06/04 数字识别码: 10.1038/s41467-019-10213-0 原文链接: 微信链接: https://mp.weixin.qq.com/s/xHcYDlJ2s7HXiUxedNVpAw 《自然-通讯》发表的一篇论文 Quantifying and predicting success in show business 报告了一种 机器学习方法, 依据演员的过往工作经历, while the percentage of a career spent active is unpredictable, productivity tends to be higher towards the beginning of a career and there are signals preceding the most productive year. Accordingly。

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