The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
이 논문은 특별한 처리 메커니즘의 필요성을 보여줍니다. 유형 (또는 종류) 다음을 포함하는 배경 지식을 바탕으로 논리 프로그램을 학습할 때의 정보 유형 계층 구조. 우리는 새로운 관계형 학습자를 개발했습니다. RHB, 이는 컴퓨팅을 처리하기 위한 특수 작업을 통합합니다. 최소한의 일반화 (LGG) 예와 코드 길이 유형이 있는 논리 프로그램의 집합입니다. FOIL, GOLEM, Progol과 같은 이전 학습자는 다음과 같이 표현되는 유형 정보를 포함하는 논리 프로그램을 생성할 수 있습니다. is_a 처지. 그러나 이 방법에는 두 가지 문제가 있습니다. 하나는 계산에 있습니다. 코드 길이 그리고 다른 하나는 공연에 있습니다. 우리는 단순히 추가하는 것을 설명하겠습니다 is_a 일반적인 문자 그대로 배경 지식과의 관계는 계산에 문제를 일으킵니다. 코드 길이 논리 프로그램의 is_a 리터럴. 인공 데이터에 대한 실험 결과, 배경지식의 종류가 많아질수록 FOIL의 학습 속도는 기하급수적으로 느려지는 것으로 나타났다. GOLEM이 생성한 가설은 RHB보다 정확도가 약 30% 정도 낮습니다. 게다가 Progol은 RHB보다 3000배 느립니다. 세 명의 학습자에 비해 RHB는 약 XNUMX개 정도를 효율적으로 처리할 수 있습니다. is_a 여전히 높은 정확도를 달성하면서 관계를 유지합니다. 이는 다음을 나타냅니다. 유형 정보는 유형이 있는 논리 프로그램을 학습할 때 특별히 처리되어야 합니다.
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Yutaka SASAKI, "On the Necessity of Special Mechanisms for Handling Types in Inductive Logic Programming" in IEICE TRANSACTIONS on Information,
vol. E82-D, no. 10, pp. 1401-1408, October 1999, doi: .
Abstract: This paper demonstrates the necessity of special handling mechanisms for type (or sort) information when learning logic programs on the basis of background knowledge that includes type hierarchy. We have developed a novel relational learner RHB, which incorporates special operations to handle the computing of the least general generalization (lgg) of examples and the code length of logic programs with types. It is possible for previous learners, such as FOIL, GOLEM and Progol, to generate logic programs that include type information represented as is_a relations. However, this expedient has two problems: one in the computation of the code length and the other in the performance. We will illustrate that simply adding is_a relations to background knowledge as ordinary literals causes a problem in computing the code length of logic programs with is_a literals. Experimental results on artificial data show that the learning speed of FOIL exponentially slows as the number of types in the background knowledge increases. The hypotheses generated by GOLEM are about 30% less accurate than those of RHB. Furthermore, Progol is two times slower than RHB. Compared to the three learners, RHB can efficiently handle about 3000 is_a relations while still achieving a high accuracy. This indicates that type information should be specially handled when learning logic programs with types.
URL: https://global.ieice.org/en_transactions/information/10.1587/e82-d_10_1401/_p
부
@ARTICLE{e82-d_10_1401,
author={Yutaka SASAKI, },
journal={IEICE TRANSACTIONS on Information},
title={On the Necessity of Special Mechanisms for Handling Types in Inductive Logic Programming},
year={1999},
volume={E82-D},
number={10},
pages={1401-1408},
abstract={This paper demonstrates the necessity of special handling mechanisms for type (or sort) information when learning logic programs on the basis of background knowledge that includes type hierarchy. We have developed a novel relational learner RHB, which incorporates special operations to handle the computing of the least general generalization (lgg) of examples and the code length of logic programs with types. It is possible for previous learners, such as FOIL, GOLEM and Progol, to generate logic programs that include type information represented as is_a relations. However, this expedient has two problems: one in the computation of the code length and the other in the performance. We will illustrate that simply adding is_a relations to background knowledge as ordinary literals causes a problem in computing the code length of logic programs with is_a literals. Experimental results on artificial data show that the learning speed of FOIL exponentially slows as the number of types in the background knowledge increases. The hypotheses generated by GOLEM are about 30% less accurate than those of RHB. Furthermore, Progol is two times slower than RHB. Compared to the three learners, RHB can efficiently handle about 3000 is_a relations while still achieving a high accuracy. This indicates that type information should be specially handled when learning logic programs with types.},
keywords={},
doi={},
ISSN={},
month={October},}
부
TY - JOUR
TI - On the Necessity of Special Mechanisms for Handling Types in Inductive Logic Programming
T2 - IEICE TRANSACTIONS on Information
SP - 1401
EP - 1408
AU - Yutaka SASAKI
PY - 1999
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E82-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 1999
AB - This paper demonstrates the necessity of special handling mechanisms for type (or sort) information when learning logic programs on the basis of background knowledge that includes type hierarchy. We have developed a novel relational learner RHB, which incorporates special operations to handle the computing of the least general generalization (lgg) of examples and the code length of logic programs with types. It is possible for previous learners, such as FOIL, GOLEM and Progol, to generate logic programs that include type information represented as is_a relations. However, this expedient has two problems: one in the computation of the code length and the other in the performance. We will illustrate that simply adding is_a relations to background knowledge as ordinary literals causes a problem in computing the code length of logic programs with is_a literals. Experimental results on artificial data show that the learning speed of FOIL exponentially slows as the number of types in the background knowledge increases. The hypotheses generated by GOLEM are about 30% less accurate than those of RHB. Furthermore, Progol is two times slower than RHB. Compared to the three learners, RHB can efficiently handle about 3000 is_a relations while still achieving a high accuracy. This indicates that type information should be specially handled when learning logic programs with types.
ER -