Extensions to the Relational Paths Based Learning Approach RPBL

Zhiqiang Gao, Zhizheng Zhang, Zhisheng Huang
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引用次数: 6

Abstract

In this paper we extend RPBL, a Relational Paths Based Learning approach for first order theories in three directions. We apply domain theories to expand structured instance space, learn recursive theories by an example of learningmember relationship of lists, and analyze the performance as well as time complexity theoretically. In addition, we give the details of our experimental results.
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基于关系路径学习方法RPBL的扩展
本文从三个方面扩展了基于关系路径的一阶理论学习方法RPBL。应用域理论扩展结构化实例空间,以学习列表的成员关系为例学习递归理论,并从理论上分析了性能和时间复杂度。此外,我们给出了我们的实验结果的细节。
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