Machine Learning by Combining Knowledge and Adaptation

结合知识和适应的机器学习

• 批准号: 9214141
• 负责人: Li Fu
• 金额: $ 16.77万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-01-01 至 1996-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9214141&HistoricalAwards=false
• 关键词: Machine; Learning; Combining; Knowledge; Adaptation

This is the first-year funding of a three-year continuing award IRI-9214141. Both rule-based and neural-network approaches have been useful for the development of intelligent systems. The main objective of this research is to develop a robust machine learning and inference system by combining these two approaches. A novel prototype system toward this objective has been implemented and tested successfully in a number of practical domains. The research project comprises of three parts centering around rule- based connectionist networks. The first is to investigate an inductive learning method which combines neural learning and symbolic artificial intelligence techniques. The second is to investigate the knowledge-based neural network approach for revising existing knowledge and learning new knowledge. The third is to investigate an incremental rule-based connectionist model for machine learning.//

这是三年连续奖的第一年资金IRI-9214141。 基于规则的和神经网络方法都对智能系统的开发都有用。 这项研究的主要目的是通过结合这两种方法来开发强大的机器学习和推理系统。 在许多实际领域中，已经成功地实施并测试了一个新的原型系统。 该研究项目包括围绕基于规则的连接派网络的三个部分。 首先是研究一种结合神经学习和符号人工智能技术的归纳学习方法。 第二个是研究基于知识的神经网络方法，以修改现有知识和学习新知识。 第三个是研究机器学习的基于增量的基于规则的连接模型。///