基于作者-主题模型的机器学习研究者推荐
Recommending Researchers in Machine Learning based on Author-Topic Model
摘要：
本文的目的是揭示研究人员在机器学习使用作者主题模型（ATM）。我们从机器学习领域的六种顶级期刊上收集了1997年至2016年发表的16，855篇科学论文，并使用ATM对其进行分析。该数据集被分解为4个区间，以识别顶级研究人员，并使用他们的相似性得分找到相似的研究人员。相似性分数使用Hellinger距离计算。研究人员使用t-SNE绘制，它降低了数据的维度，同时保持点之间的距离相同。对我们研究的分析有助于即将到来的研究人员找到他们感兴趣领域的顶级研究人员。
英文摘要：
The aim of this paper is to uncover the researchers in machine learning using the author-topic model (ATM). We collect 16,855 scientific papers from six top journals in the field of machine learning published from 1997 to 2016 and analyze them using ATM. The dataset is broken down into 4 intervals to identify the top researchers and find similar researchers using their similarity score. The similarity score is calculated using Hellinger distance. The researchers are plotted using t-SNE, which reduces the dimensionality of the data while keeping the same distance between the points. The analysis of our study helps the upcoming researchers to find the top researchers in their area of interest.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
