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Print ISSN: 1582-7445
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doi: 10.4316/AECE


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  4/2022 - 6

Hyper-parameter Tuning for Quantum Support Vector Machine

DEMIRTAS, F. See more information about DEMIRTAS, F. on SCOPUS See more information about DEMIRTAS, F. on IEEExplore See more information about DEMIRTAS, F. on Web of Science, TANYILDIZI, E. See more information about TANYILDIZI, E. on SCOPUS See more information about TANYILDIZI, E. on SCOPUS See more information about TANYILDIZI, E. on Web of Science
 
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Download PDF pdficon (1,442 KB) | Citation | Downloads: 486 | Views: 1,005

Author keywords
grid computing, optimization, parameter estimation, quantum computing, support vector machines

References keywords
quantum(22), learning(16), machine(15), vector(10), support(10), optimization(8), neural(7), systems(6), kernel(6), search(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2022-11-30
Volume 22, Issue 4, Year 2022, On page(s): 47 - 54
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2022.04006
Web of Science Accession Number: 000920289700006
SCOPUS ID: 85150218461

Abstract
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In recent years, the positive effect of quantum techniques on machine learning methods have been studied. Especially in training big data, quantum computing is beneficial in terms of speed. This study examined and applied the Quantum Support Vector Machine steps to the breast cancer dataset. Different types of feature maps used in the conversion of a classical dataset to a quantum dataset were examined using different dimensions. One of the factors that directly affect the performance of machine learning models is the correct selection of the hyper-parameters. These values must be obtained independent from the designer. Within the scope of the study, the hyper-parameter tuning methods, namely, Grid, Random, and Bayesian search methods, were examined. By using these methods, the hyper-parameters of the Support vector machine, which is one of the machine learning methods, were found. The performances of Linear, Non-linear and Quantum support vector machines were compared, and the running costs were analyzed.


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References Weight

Web of Science® Citations for all references: 46,070 TCR
SCOPUS® Citations for all references: 18,701 TCR

Web of Science® Average Citations per reference: 1,097 ACR
SCOPUS® Average Citations per reference: 445 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2023-11-29 20:43 in 219 seconds.




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