Learnability in Query and Restricted Distribution Models

查询和限制分布模型的可学习性

• 批准号: 9210957
• 负责人: Lisa Hellerstein
• 金额: $ 5.72万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-07-01 至 1996-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9210957&HistoricalAwards=false
• 关键词: Learnability; Query; Restricted; Distribution; Models

The learnability of classes of functions in various formal learning models will be studied. There have been many negative results on learning even simple classes of functions in the Valiant PAC model. One goal of this research is to develop algorithms for learning relatively complex classes of functions in other learning models. A related goal is to determine the relative power of some of these learning models. Also proposed is the study of the relation between learnability of a class of functions, structural properties of the class, and the complexity of decision problems involving the class, in order to discover which classes of functions are learnable, by what means they are learnable, and what inherent qualities make them learnable.

各种形式的函数类的可学习性 学习模式将被研究。 发生多 即使是简单的函数类， 在Valiant PAC模型中。 这项研究的一个目标是 开发学习相对复杂类的算法， 在其他学习模式中的作用。 一个相关的目标是 确定这些学习模型的相对能力。 还提出了研究之间的关系， 一类函数的可学习性， 类，以及决策问题的复杂性， 类，以发现哪些类的功能是 可学习的，通过什么方式可学习的，以及固有的 品质使他们可以学习。