Cognitive Complexity and Analogies in Transfer Learning
Cognitive Complexity and Analogies in Transfer Learning
复制标题
迁移学习中的认知复杂性和类比
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
G. Strube
中科院分区：
文献类型：
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作者：
Marco Ragni;G. Strube
摘要学习能力往往需要迁移关系
知识从一个领域转移到另一个领域。人类和计算机很难识别各自的源域，从中可以将关系特征应用于目标域。人类推理困难的另一个来源是转换的复杂性
功能。在本文中，我们研究了两个需要识别关系模式和变换函数的领域：数列和几何类比问题。介绍了人类过程的特征并讨论了现有的认知模型。
AbstractThe ability to learn often requires transferring relational
knowledge from one domain to another. It is difficult for humans and computers to identify the respective source domain from which relational characteristics can be applied to the target domain. An additional source of human reasoning difficulty is the complexity of the transformation
function. In this article we investigate two domains in which the identification of relational patterns and of a transformation function are necessary: number series and geometrical analogy problems. Characteristics of the human processes are presented and existing cognitive models are discussed.