Learning horn expressions with LOGAN-H

作     者：Arias, Marta Khardon, Roni Maloberti, Jerome 

作者机构：Columbia Univ Ctr Computat Learning Syst New York NY 10115 USA Tufts Univ Dept Comp Sci Medford MA 02155 USA Univ Paris Sud Rech Informat Lab F-91405 Orsay France 

出 版 物：《JOURNAL OF MACHINE LEARNING RESEARCH》 (机器学习研究杂志)

年 卷 期：2007年第8卷第3期

页      面：549-587页

核心收录：

学科分类：08[工学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：inductive logic programming subsumption bottom-up learning learning with queries 

摘      要：The paper introduces LOGAN-H-a system for learning first-order function-free Horn expressions from interpretations. The system is based on an algorithm that learns by asking questions and that was proved correct in previous work. The current paper shows how the algorithm can be implemented in a practical system, and introduces a new algorithm based on it that avoids interaction and learns from examples only. The LOGAN-H system implements these algorithms and adds several facilities and optimizations that allow efficient applications in a wide range of problems. As one of the important ingredients, the system includes several fast procedures for solving the subsumption problem, an NP-complete problem that needs to be solved many times during the learning process. We describe qualitative and quantitative experiments in several domains. The experiments demonstrate that the system can deal with varied problems, large amounts of data, and that it achieves good classification accuracy.