SGER: Concise, Intelligble, and Approximate Modeling of Large Classifications in Natural and Social Science

SGER：自然和社会科学大分类的简洁、可理解和近似建模

• 批准号: 9819340
• 负责人: Raul Valdes-Perez
• 金额: $ 6.5万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-12-15 至 1999-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9819340&HistoricalAwards=false
• 关键词: SGER; Concise; Intelligble; Approximate; Modeling

In this exploratory research, an attempt is being made to understand the generic scientific task of classification. This task is perhaps somewhat better understood than discovery, which has previously been studied by the same PI. In the AI field, much research is going on in induction of classifications and in classification learning. But it is important to augment what is discovered in these areas by looking at two other important aspects of classification: describing classifications (i.e., developing a good model of the classification scheme), and relating a classification to new criteria (e.g., relating a biological classification based on characteristics or serology to one based on DNA). This work will lead to a better understanding of the cognitive aspects of classification, as well as to new computational tools for aspects of classification that can be used by scientists in a variety of disciplines.

在这项探索性研究中，试图理解分类的一般科学任务。这个任务可能比发现更好理解，发现以前由同一个PI研究过。在人工智能领域，分类的归纳和分类学习正在进行大量的研究。但重要的是，通过研究分类的另外两个重要方面来扩大在这些领域中发现的内容：描述分类（即，开发分类方案的良好模型），以及将分类与新标准相关联（例如，将基于特征或血清学的生物分类与基于DNA的生物分类相关联）。这项工作将导致更好地理解分类的认知方面，以及新的计算工具，可用于科学家在各种学科的分类方面。