Contribution to a conference proceedings

FZJ-2021-01283

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Approaching Remote Sensing Image Classification with Ensembles of Support Vector Machines on the D-Wave Quantum Annealer

Cavallaro, G. (Corresponding author)FZJ* ; Willsch, D.FZJ* ; Willsch, M.FZJ* ; Michielsen, K.FZJ* ; Riedel, M.FZJ*

2020
IEEE

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Please use a persistent id in citations: http://hdl.handle.net/2128/27294  doi:10.1109/IGARSS39084.2020.9323544

Abstract: Support Vector Machine (SVM) is a popular supervised MachineLearning (ML) method that is widely used for classificationand regression problems. Recently, a method to trainSVMs on a D-Wave 2000Q Quantum Annealer (QA) was proposedfor binary classification of some biological data. First,ensembles of weak quantum SVMs are generated by trainingeach classifier on a disjoint training subset that can be fitinto the QA. Then, the computed weak solutions are fusedfor making predictions on unseen data. In this work, the classificationof Remote Sensing (RS) multispectral images withSVMs trained on a QA is discussed. Furthermore, an opencode repository is released to facilitate an early entry into thepractical application of QA, a new disruptive compute technology.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 512 - Data-Intensive Science and Federated Computing (POF3-512) (POF3-512)

Appears in the scientific report 2021

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Database coverage:
; ; BIOSIS Previews ; Current Contents - Life Sciences ; Ebsco Academic Search ; IF < 5 ; JCR ; NCBI Molecular Biology Database ; SCOPUS ; Science Citation Index Expanded ; Thomson Reuters Master Journal List ; Web of Science Core Collection ; Zoological Record

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