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AN ALTERNATIVE METHOD OF CONCEPT LEARNING

Part of:Artificial intelligence (68Txx)

Published online by Cambridge University Press:  06 March 2017

SEN WANGOpen the ORCID record for SEN WANG[Opens in a new window],
QINGXIANG FANGOpen the ORCID record for QINGXIANG FANG[Opens in a new window] and
JUN-E FENGOpen the ORCID record for JUN-E FENG[Opens in a new window]
SEN WANG
Affiliation:
School of Mathematics, Shandong University, Jinan 250100, China email 17862988175@163.com, fengjune@sdu.edu.cn
QINGXIANG FANG
Affiliation:
School of Science, China Jiliang University, Hangzhou 310018, China email fangqx@cjlu.edu.cn
JUN-E FENG*
Affiliation:
School of Mathematics, Shandong University, Jinan 250100, China email 17862988175@163.com, fengjune@sdu.edu.cn
*
✉ fengjune@sdu.edu.c
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Abstract

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We solve the problem of concept learning using a semi-tensor product method. All possible hypotheses are expressed under the framework of a semi-tensor product. An algorithm is raised to derive the version space. In some cases, the new approach improves the efficiency compared to the previous approach.

Keywords

concept learningversion spacesemi-tensor producttarget conceptall possible hypotheses

MSC classification

Primary:68T01: General

Information

Type
Research Article
Information
The ANZIAM Journal , Volume 58 , Issue 3-4 , April 2017, pp. 211 - 219
Copyright
© 2017 Australian Mathematical Society 

References

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