Is the Whole Greater Than the Sum of Its Parts?[整体大于部分之和吗?Is the Whole Greater Than the Sum of Its Parts?[整体大于部分之和吗? |
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课程网址: | http://videolectures.net/kdd2017_li_the_whole/ |
主讲教师: | Liangyue Li |
开课单位: | 亚利桑那州立大学 |
开课时间: | 2017-10-09 |
课程语种: | 英语 |
中文简介: | 部分与整体的关系经常出现在许多学科中,从协作团队、众包、自治系统到网络系统。从算法的角度来看,现有的工作主要集中在通过单独的模型或线性联合模型来预测整体和部分的结果,这些模型假设部分的结果对整体的结果具有线性且独立的影响。在本文中,我们提出了一种名为 PAROLE 的联合预测方法,可以同时、相互预测部分和整体结果。与现有工作相比,所提出的方法具有两个明显的优势。首先(模型通用性),我们将联合部分整体结果预测制定为通用优化问题,它能够编码整体和部分结果之间的各种复杂关系,超出线性独立假设。其次(算法效率),我们提出了一种有效且高效的块坐标下降算法,该算法能够在时间和空间上以线性复杂度找到坐标最优。对现实世界数据集的广泛实证评估表明,所提出的 PAROLE (1) 通过对非线性部分-整体关系以及部分-部分相互依赖性进行建模,可以带来一致的预测性能改进,(2) 根据训练数据集的大小。 |
课程简介: | The part-whole relationship routinely finds itself in many disciplines, ranging from collaborative teams, crowdsourcing, autonomous systems to networked systems. From the algorithmic perspective, the existing work has primarily focused on predicting the outcomes of the whole and parts, by either separate models or linear joint models, which assume the outcome of the parts has a linear and independent effect on the outcome of the whole. In this paper, we propose a joint predictive method named PAROLE to simultaneously and mutually predict the part and whole outcomes. The proposed method offers two distinct advantages over the existing work. First (Model Generality), we formulate joint part-whole outcome prediction as a generic optimization problem, which is able to encode a variety of complex relationships between the outcome of the whole and parts, beyond the linear independence assumption. Second (Algorithm Efficacy), we propose an effective and efficient block coordinate descent algorithm, which is able to find the coordinate-wise optimum with a linear complexity in both time and space. Extensive empirical evaluations on real-world datasets demonstrate that the proposed PAROLE (1) leads to consistent prediction performance improvement by modeling the non-linear part-whole relationship as well as part-part interdependency, and (2) scales linearly in terms of the size of the training dataset. |
关 键 词: | 部分与整体; 线性联合模型; 数据科学 |
课程来源: | 视频讲座网 |
数据采集: | 2023-12-27:wujk |
最后编审: | 2023-12-27:wujk |
阅读次数: | 25 |