A dual-mode learning mechanism combining knowledge-education and machine-learning

Yichang Chen; An-Pin Chen

doi:10.1007/978-3-540-87732-5-11

A dual-mode learning mechanism combining knowledge-education and machine-learning

Yichang Chen^*, An-Pin Chen

^*Corresponding author for this work

Institute of Information Management

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

From 1956, the definitions of learning according to Artificial Intelligence and Psychology to human mind/behavior are obviously different. Owing to the rapid development of the computing power, we have potential to enhance the learning mechanism of AI. This work tries to discuss the learning process from the traditional AI learning models which are almost based on trial and error style. Furthermore, some relative literatures have pointed out that teaching-base education would increase the learning efficiency better than trial and error style. That is the reason we enhance the learning process to propose a dual-perspective learning mechanism, E&R-R XCS. As for XCS is a better accuracy model of AI, we have applied it as a basement and proposed to develop an intelligence-learning model. Finally, this work will give the inference discussion about the accuracy and accumulative performance of XCS, R-R XCS, and E&R-R XCS respectively, and the obvious summary would be concluded. That is, the proposed dual-learning mechanism has enhanced successfully.

Original language	English
Title of host publication	Advances in Neural Networks - ISNN 2008 - 5th International Symposium on Neural Networks, ISNN 2008, Proceedings
Pages	87-96
Number of pages	10
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-540-87732-5-11
State	Published - 1 Dec 2008
Event	5th International Symposium on Neural Networks, ISNN 2008 - Beijing, China Duration: 24 Sep 2008 → 28 Sep 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	5263 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th International Symposium on Neural Networks, ISNN 2008
Country	China
City	Beijing
Period	24/09/08 → 28/09/08

Keywords

Artificial intelligence
Intelligence-learning
Psychology
Teaching-base education
Trial and error

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Chen, Y., & Chen, A-P. (2008). A dual-mode learning mechanism combining knowledge-education and machine-learning. In Advances in Neural Networks - ISNN 2008 - 5th International Symposium on Neural Networks, ISNN 2008, Proceedings (PART 1 ed., pp. 87-96). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5263 LNCS, No. PART 1). https://doi.org/10.1007/978-3-540-87732-5-11

Chen, Yichang ; Chen, An-Pin. / A dual-mode learning mechanism combining knowledge-education and machine-learning. Advances in Neural Networks - ISNN 2008 - 5th International Symposium on Neural Networks, ISNN 2008, Proceedings. PART 1. ed. 2008. pp. 87-96 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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title = "A dual-mode learning mechanism combining knowledge-education and machine-learning",

abstract = "From 1956, the definitions of learning according to Artificial Intelligence and Psychology to human mind/behavior are obviously different. Owing to the rapid development of the computing power, we have potential to enhance the learning mechanism of AI. This work tries to discuss the learning process from the traditional AI learning models which are almost based on trial and error style. Furthermore, some relative literatures have pointed out that teaching-base education would increase the learning efficiency better than trial and error style. That is the reason we enhance the learning process to propose a dual-perspective learning mechanism, E&R-R XCS. As for XCS is a better accuracy model of AI, we have applied it as a basement and proposed to develop an intelligence-learning model. Finally, this work will give the inference discussion about the accuracy and accumulative performance of XCS, R-R XCS, and E&R-R XCS respectively, and the obvious summary would be concluded. That is, the proposed dual-learning mechanism has enhanced successfully.",

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Chen, Y & Chen, A-P 2008, A dual-mode learning mechanism combining knowledge-education and machine-learning. in Advances in Neural Networks - ISNN 2008 - 5th International Symposium on Neural Networks, ISNN 2008, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 5263 LNCS, pp. 87-96, 5th International Symposium on Neural Networks, ISNN 2008, Beijing, China, 24/09/08. https://doi.org/10.1007/978-3-540-87732-5-11

A dual-mode learning mechanism combining knowledge-education and machine-learning. / Chen, Yichang; Chen, An-Pin.

Advances in Neural Networks - ISNN 2008 - 5th International Symposium on Neural Networks, ISNN 2008, Proceedings. PART 1. ed. 2008. p. 87-96 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5263 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Chen, An-Pin

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AB - From 1956, the definitions of learning according to Artificial Intelligence and Psychology to human mind/behavior are obviously different. Owing to the rapid development of the computing power, we have potential to enhance the learning mechanism of AI. This work tries to discuss the learning process from the traditional AI learning models which are almost based on trial and error style. Furthermore, some relative literatures have pointed out that teaching-base education would increase the learning efficiency better than trial and error style. That is the reason we enhance the learning process to propose a dual-perspective learning mechanism, E&R-R XCS. As for XCS is a better accuracy model of AI, we have applied it as a basement and proposed to develop an intelligence-learning model. Finally, this work will give the inference discussion about the accuracy and accumulative performance of XCS, R-R XCS, and E&R-R XCS respectively, and the obvious summary would be concluded. That is, the proposed dual-learning mechanism has enhanced successfully.

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KW - Teaching-base education

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Chen Y, Chen A-P. A dual-mode learning mechanism combining knowledge-education and machine-learning. In Advances in Neural Networks - ISNN 2008 - 5th International Symposium on Neural Networks, ISNN 2008, Proceedings. PART 1 ed. 2008. p. 87-96. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). https://doi.org/10.1007/978-3-540-87732-5-11