Theory Revision and Related Problems in Learning Theory

学习理论的理论修正及相关问题

• 批准号: 0100040
• 负责人: Judith Goldsmith
• 金额: $ 21.33万
• 依托单位: University of Kentucky Research Foundation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-08-15 至 2005-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0100040&HistoricalAwards=false
• 关键词: Theory; Revision; Related; Problems; Learning

Theory revision is the correcting of a given, roughly correct rule,also known as a concept or theory. This problem arises frequently inmachine learning, for instance, when the initial output of an expert system is not correct, and when the machine learningproblem is too large or too complex to solve from scratch, and anapproximately correct rule is needed to jump-start the learningprocess. There has been considerable ad hoc building of theory revision systems, but the theory is poorly understood. This researchinvestigates fundamental mathematical possibilities and limitationsof efficient theory revision. It is hoped that as a result ofthis research, theory revision in computational learning theory willemerge as a general framework for the study of learning situationswhere a large amount of initial information is available ornecessary. In particular, the PIs investigate the following areas: extensionsof their previous work on propositional logic theory revisionwith queries, relations to certificate complexity andattribute-efficient learning, revision problems for predicate logicrepresentations, and both the learning and revising of categorial grammars.

理论修正是对一个给定的，大致正确的规则的修正，也称为概念或理论。这个问题在机器学习中经常出现，例如，当专家系统的初始输出不正确时，当机器学习问题太大或太复杂而无法从头开始解决时，需要一个近似正确的规则来启动学习过程。理论修正体系已经有了相当大的建立，但人们对理论的理解却很差。本研究探讨了有效理论修正的基本数学可能性和局限性。希望通过这项研究，计算学习理论中的理论修正将成为研究大量初始信息可用或必要的学习情境的一般框架。特别是，pi研究了以下领域：他们以前在命题逻辑理论修正方面的工作的扩展-查询，与证书复杂性和属性有效学习的关系，谓词逻辑表示的修正问题，以及范畴语法的学习和修正。