9789819974412 - active learning to minimize the possible risk of future epidemics di santosh, kc; nakarmi, suprim (10 risultati)
Lingua: Inglese
Editore: Springer, 2023
Serie: Libro 309 di 472 - SpringerBriefs in Applied Sciences and Technology
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Editore: Springer Nature, 2023
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Paperback. Condizione: Brand New. 112 pages. 9.26x6.10x0.24 inches. In Stock.
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Lingua: Inglese
Editore: Springer, Springer, 2023
Serie: Libro 309 di 472 - SpringerBriefs in Applied Sciences and Technology
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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Future epidemics are inevitable, and it takes months and even years to collect fully annotated data. The sheer magnitude of data required for machine learning algorithms, spanning both shallow and deep structures, raises a fundamental question: ho…w big data is big enough to effectively tackle future epidemics In this context, active learning, often referred to as human or expert-in-the-loop learning, becomes imperative, enabling machines to commence learning from day one with minimal labeled data. In unsupervised learning, the focus shifts toward constructing advanced machine learning models like deep structured networks that autonomously learn over time, with human or expert intervention only when errors occur and for limited data-a process we term mentoring. In the context of Covid-19, this book explores the use of deep features to classify data into two clusters (0/1: Covid-19/non-Covid-19) across three distinct datasets: cough sound, Computed Tomography (CT) scan, and chest x-ray (CXR). Not to be confused, our primary objective is to provide a strong assertion on how active learning could potentially be used to predict disease from any upcoming epidemics. Upon request (education/training purpose), GitHub source codes are provided.
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Lingua: Inglese
Editore: Springer, 2024
Serie: Libro 309 di 472 - SpringerBriefs in Applied Sciences and Technology
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Taschenbuch. Condizione: Neu. Active Learning to Minimize the Possible Risk of Future Epidemics | Kc Santosh (u. a.) | Taschenbuch | SpringerBriefs in Applied Sciences and Technology | xvi | Englisch | 2024 | Springer | EAN 9789819974412 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelbe…rg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Lingua: Inglese
Editore: Springer, 2023
Serie: Libro 309 di 472 - SpringerBriefs in Applied Sciences and Technology
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Condizione: new. Questo è un articolo print on demand.
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Serie: Libro 309 di 472 - SpringerBriefs in Applied Sciences and Technology
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Lingua: Inglese
Editore: Springer Nature Singapore Dez 2023, 2023
Serie: Libro 309 di 472 - SpringerBriefs in Applied Sciences and Technology
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Future epidemics are inevitable, and it takes months and even years to collect fully annotated data. The sheer magnitude of data required for machine learning algorithms, spanning both shallow and deep structures, raises a fundamen…tal question: how big data is big enough to effectively tackle future epidemics In this context, active learning, often referred to as human or expert-in-the-loop learning, becomes imperative, enabling machines to commence learning from day one with minimal labeled data. In unsupervised learning, the focus shifts toward constructing advanced machine learning models like deep structured networks that autonomously learn over time, with human or expert intervention only when errors occur and for limited data-a process we term mentoring. In the context of Covid-19, this book explores the use of deep features to classify data into two clusters (0/1: Covid-19/non-Covid-19) across three distinct datasets: cough sound, Computed Tomography (CT) scan, and chest x-ray (CXR). Not to be confused, our primary objective is to provide a strong assertion on how active learning could potentially be used to predict disease from any upcoming epidemics. Upon request (education/training purpose), GitHub source codes are provided. 112 pp. Englisch.
Lingua: Inglese
Editore: Springer, 2023
Serie: Libro 309 di 472 - SpringerBriefs in Applied Sciences and Technology
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Condizione: New. PRINT ON DEMAND pp. 112.
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Editore: Springer, Berlin|Springer Nature Singapore|Springer, 2024
Serie: Libro 309 di 472 - SpringerBriefs in Applied Sciences and Technology
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Da: moluna, Greven, Germaniamoluna
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Future epidemics are inevitable, and it takes months and even years to collect fully annotated data. The sheer magnitude of data required for machine learning algorithms, spanning both shallow and deep structures, ra…ises a fundamental question: how big d.
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Lingua: Inglese
Editore: Springer, Springer Nov 2023, 2023
Serie: Libro 309 di 472 - SpringerBriefs in Applied Sciences and Technology
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Future epidemics are inevitable, and it takes months and even years to collect fully annotated data. The sheer magnitude of data required for machine learning algorithms, spanning both shallow and deep structures, raises a fundamental…question: how big data is big enough to effectively tackle future epidemics In this context, active learning, often referred to as human or expert-in-the-loop learning, becomes imperative, enabling machines to commence learning from day one with minimal labeled data. In unsupervised learning, the focus shifts toward constructing advanced machine learning models like deep structured networks that autonomously learn over time, with human or expert intervention only when errors occur and for limited datäa process we term mentoring. In the context of Covid-19, this book explores the use of deep features to classify data into two clusters (0/1: Covid-19/non-Covid-19) across three distinct datasets: cough sound, Computed Tomography (CT) scan, and chest x-ray (CXR). Not to be confused, our primary objective is to provide a strong assertion on how active learning could potentially be used to predict disease from any upcoming epidemics. Upon request (education/training purpose), GitHub source codes are provided.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 112 pp. Englisch.
