Quantum Ensemble for Classification
Quantum Ensemble for Classification
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Publisher
Ithaca: Cornell University Library, arXiv.org
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Language
English
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Publisher
Ithaca: Cornell University Library, arXiv.org
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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...
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Full title
Quantum Ensemble for Classification
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Author / Creator
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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