Evaluating models of category learning that use general feature-based representations

评估使用基于通用特征的表示的类别学习模型

• 批准号: ARC : DP0211406
• 负责人: Michael Lee
• 金额: $ 4.5万
• 依托单位: The University of Adelaide
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2002
• 资助国家: 澳大利亚
• 起止时间: 2002-01-01 至 2003-12-31
• 项目状态: 已结题
• 来源: https://researchdata.edu.au/evaluating-models-category-based-representations/74344
• 关键词: Evaluating; models; category; learning; use

Evaluating models of category learning that use general feature-based representations. Three competing models of human category learning will be evaluated by comparing their behaviour to human performance on an experimental task where each model makes qualitatively different predictions. A series of theoretical and algorithmic advances will be undertaken to ensure each of the category learning models uses the same feature-based representation. Because the three models propose very different learning processes, their comparison will give insight into the basic cognitive process of categorisation. The algorithms for generating feature representations and modelling human category learning will also have potential for application in data visualisation and information handling systems.

评估使用一般特征表示的类别学习模型。人类类别学习的三个竞争模型将通过比较它们的行为与人类在实验任务中的表现来评估，每个模型都做出了质的不同预测。一系列的理论和算法的进步将进行，以确保每个类别学习模型使用相同的基于特征的表示。由于这三个模型提出了非常不同的学习过程，它们的比较将使我们深入了解分类的基本认知过程。用于生成特征表示和建模人类类别学习的算法也将在数据可视化和信息处理系统中具有应用潜力。