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International Conference on Machine Learning and Cybernetics

作者： Zheng, L Jia, D Liu, CN Beijing Polytech Univ Sch Comp Sci Beijing Municipal Key Lab Multimedia & Intelligen Beijing 100022 Peoples R China

ISBN: (纸本)0780378652

A continuing problem with inductive logic programming (ILP) has proved to be difficult to handle. constraint inductive logic programming (CILP) aims to solve this problem with ILP. We propose a new approach to CILP, and implement a prototype of CILP system called BPU-CILP. In our approach, methods from pattern recognition, such as Fisher's linear discriminant and prototype-based partitional clustering, are introduced to CILP. BPU-CILP can generate various forms of polynomial constraints in multiple dimensions, without additional background knowledge. As results, the CLP program covering all positive examples and consisting with all negative examples can be automatically derived.

关键词： constraint inductive logic programming patternrecognition clustering

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Generating numerical constraints in CILP

引用

INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2005年第1期19卷 91-108页

作者： Zheng, L Liu, CN Jia, L Zhong, N Beijing Univ Technol Coll Comp Sci Beijing Municipal Key Lab Multimed & Intelligent Beijing 100022 Peoples R China Maebashi Inst Technol Dept Informat Engn Maebashi Gumma 3710816 Japan

A continuing problem with inductive logic programming (ILP) has been the poor handling of numbers. constraint inductive logic programming (CILP) aims to solve this problem with ILP. We propose a new approach to generating numerical constraints in CILP, and describe an implementation of the CILP system (namely, BPU-CILP). In our approach, methods from pattern recognition and multivariate data analysis, such as Fisher's linear discriminant, dynamic clustering and principal component analysis, are introduced into CILP. The BPU-CILP can generate various forms of polynomial constraints of multiple dimensions, without additional background knowledge. As a result, the constraint logic program covering all positive examples and consistent with all negative examples can be derived automatically.

关键词： inductive logic programming constraint inductive logic programming pattern recognition multivariate statistical analysis

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