Refining Theory with Multiple Faults

Somkiat TANGKITVANICH; Masamichi SHIMURA

Refining Theory with Multiple Faults

Somkiat TANGKITVANICH, Masamichi SHIMURA

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This paper presents a system that automatically refines the theory expressed in the function-free first-order logic. Our system can efficiently correct multiple faults in both the concept and subconcepts of the theory, given only the classified examples of the concept. It can refine larger classes of theory than existing systems can since it has overcome many of their limitations. Our system is based on a new combination of an inductive and an explanation-based learning algorithms, which we call the biggest-first multiple-example EBL (BM-EBL). From a learning perspective, our system is an improvement over the FOIL learning system in that our system can accept a theory as well as examples. An experiment shows that when our system is given a theory that has the classification error rate as high as 50%, it can still learn faster and with more accuracy than when it is not given any theory.

Publication: IEICE TRANSACTIONS on Information Vol.E75-D No.4 pp.470-476

Publication Date: 1992/07/25

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Type of Manuscript: Special Section PAPER (Special Issue on Algorithmic Learning Theory)

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Somkiat TANGKITVANICH, Masamichi SHIMURA, "Refining Theory with Multiple Faults" in IEICE TRANSACTIONS on Information, vol. E75-D, no. 4, pp. 470-476, July 1992, doi: .
Abstract: This paper presents a system that automatically refines the theory expressed in the function-free first-order logic. Our system can efficiently correct multiple faults in both the concept and subconcepts of the theory, given only the classified examples of the concept. It can refine larger classes of theory than existing systems can since it has overcome many of their limitations. Our system is based on a new combination of an inductive and an explanation-based learning algorithms, which we call the biggest-first multiple-example EBL (BM-EBL). From a learning perspective, our system is an improvement over the FOIL learning system in that our system can accept a theory as well as examples. An experiment shows that when our system is given a theory that has the classification error rate as high as 50%, it can still learn faster and with more accuracy than when it is not given any theory.
URL: https://global.ieice.org/en_transactions/information/10.1587/e75-d_4_470/_p

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@ARTICLE{e75-d_4_470,
author={Somkiat TANGKITVANICH, Masamichi SHIMURA, },
journal={IEICE TRANSACTIONS on Information},
title={Refining Theory with Multiple Faults},
year={1992},
volume={E75-D},
number={4},
pages={470-476},
abstract={This paper presents a system that automatically refines the theory expressed in the function-free first-order logic. Our system can efficiently correct multiple faults in both the concept and subconcepts of the theory, given only the classified examples of the concept. It can refine larger classes of theory than existing systems can since it has overcome many of their limitations. Our system is based on a new combination of an inductive and an explanation-based learning algorithms, which we call the biggest-first multiple-example EBL (BM-EBL). From a learning perspective, our system is an improvement over the FOIL learning system in that our system can accept a theory as well as examples. An experiment shows that when our system is given a theory that has the classification error rate as high as 50%, it can still learn faster and with more accuracy than when it is not given any theory.},
keywords={},
doi={},
ISSN={},
month={July},}

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TY - JOUR
TI - Refining Theory with Multiple Faults
T2 - IEICE TRANSACTIONS on Information
SP - 470
EP - 476
AU - Somkiat TANGKITVANICH
AU - Masamichi SHIMURA
PY - 1992
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E75-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - July 1992
AB - This paper presents a system that automatically refines the theory expressed in the function-free first-order logic. Our system can efficiently correct multiple faults in both the concept and subconcepts of the theory, given only the classified examples of the concept. It can refine larger classes of theory than existing systems can since it has overcome many of their limitations. Our system is based on a new combination of an inductive and an explanation-based learning algorithms, which we call the biggest-first multiple-example EBL (BM-EBL). From a learning perspective, our system is an improvement over the FOIL learning system in that our system can accept a theory as well as examples. An experiment shows that when our system is given a theory that has the classification error rate as high as 50%, it can still learn faster and with more accuracy than when it is not given any theory.
ER -