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Quantum Ensemble for Classification

Quantum Ensemble for Classification

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2419780176

Quantum Ensemble for Classification

About this item

Full title

Quantum Ensemble for Classification

Publisher

Ithaca: Cornell University Library, arXiv.org

Journal title

arXiv.org, 2022-01

Language

English

Formats

Publication information

Publisher

Ithaca: Cornell University Library, arXiv.org

More information

Scope and Contents

Contents

A powerful way to improve performance in machine learning is to construct an ensemble that combines the predictions of multiple models. Ensemble methods are often much more accurate and lower variance than the individual classifiers that make them up but have high requirements in terms of memory and computational time. In fact, a large number of al...

Alternative Titles

Full title

Quantum Ensemble for Classification

Authors, Artists and Contributors

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_proquest_journals_2419780176

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2419780176

Other Identifiers

E-ISSN

2331-8422

DOI

10.48550/arxiv.2007.01028

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