机器学习和扎根理论方法:收敛、发散和组合

Michael J. Muller, Shion Guha, E. Baumer, David Mimno, N. Shami
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引用次数: 72

摘要

扎根理论方法(GTM)和机器学习(ML)通常被认为是完全不同的。在本文中,我们将探讨这些方法之间意想不到的收敛。我们提出了新的研究方向,可以进一步阐明这些方法之间的关系,并可以利用这些关系来增强我们描述现象的能力,并发展更强大的混合理论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning and Grounded Theory Method: Convergence, Divergence, and Combination
Grounded Theory Method (GTM) and Machine Learning (ML) are often considered to be quite different. In this note, we explore unexpected convergences between these methods. We propose new research directions that can further clarify the relationships between these methods, and that can use those relationships to strengthen our ability to describe our phenomena and develop stronger hybrid theories.
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