Articoli correlati a Improving Classifier Generalization: Real-time Machine...
- EditoreSpringer-Nature New York Inc
- Data di pubblicazione2022
- ISBN 10 9811950725
- ISBN 13 9789811950728
- RilegaturaCopertina rigida
- LinguaInglese
- Numero edizione1
- Numero di pagine192
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Improving Classifier Generalization : Real-Time Machine Learning based Applications
Editore:
Springer Nature Singapore, 2022
ISBN 10: 9811950725
ISBN 13: 9789811950728
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Da: Buchpark, Trebbin, Germania
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Condizione: Hervorragend. Zustand: Hervorragend | Seiten: 192 | Sprache: Englisch | Produktart: Bücher. Codice articolo 40310564/1
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Improving Classifier Generalization
Editore:
Springer Nature Singapore Sep 2022, 2022
ISBN 10: 9811950725
ISBN 13: 9789811950728
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book elaborately discusses techniques commonly used to improve generalization performance in classification approaches. The contents highlight methods to improve classification performance in numerous case studies: ranging from datasets of UCI repository to predictive maintenance problems and cancer classification problems. The book specifically provides a detailed tutorial on how to approach time-series classification problems and discusses two real time case studies on condition monitoring. In addition to describing the various aspects a data scientist must consider before finalizing their approach to a classification problem and reviewing the state of the art for improving classification generalization performance, it also discusses in detail the authors own contributions to the field, including MVPC - a classifier with very low VC dimension, a graphical indices based framework for reliable predictive maintenance and a novel general-purpose membership functions for Fuzzy Support Vector Machine which provides state of the art performance with noisy datasets, and a novel scheme to introduce deep learning in Fuzzy Rule based classifiers (FRCs). This volume will serve as a useful reference for researchers and students working on machine learning, health monitoring, predictive maintenance, time-series analysis, gene-expression data classification. 192 pp. Englisch. Codice articolo 9789811950728
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Improving Classifier Generalization
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Springer, Berlin|Springer Nature Singapore|Springer, 2022
ISBN 10: 9811950725
ISBN 13: 9789811950728
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Da: moluna, Greven, Germania
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book elaborately discusses techniques commonly used to improve generalization performance in classification approaches. The contents highlight methods to improve classification performance in numerous case studies: ranging from datasets of UCI repos. Codice articolo 628808176
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Improving Classifier Generalization : Real-Time Machine Learning based Applications
Editore:
Springer Nature Singapore, Springer Nature Singapore, 2022
ISBN 10: 9811950725
ISBN 13: 9789811950728
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Da: AHA-BUCH GmbH, Einbeck, Germania
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Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book elaborately discusses techniques commonly used to improve generalization performance in classification approaches. The contents highlight methods to improve classification performance in numerous case studies: ranging from datasets of UCI repository to predictive maintenance problems and cancer classification problems. The book specifically provides a detailed tutorial on how to approach time-series classification problems and discusses two real time case studies on condition monitoring. In addition to describing the various aspects a data scientist must consider before finalizing their approach to a classification problem and reviewing the state of the art for improving classification generalization performance, it also discusses in detail the authors own contributions to the field, including MVPC - a classifier with very low VC dimension, a graphical indices based framework for reliable predictive maintenance and a novel general-purpose membership functions for Fuzzy Support Vector Machine which provides state of the art performance with noisy datasets, and a novel scheme to introduce deep learning in Fuzzy Rule based classifiers (FRCs). This volume will serve as a useful reference for researchers and students working on machine learning, health monitoring, predictive maintenance, time-series analysis, gene-expression data classification. Codice articolo 9789811950728
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Improving Classifier Generalization (Hardcover)
Editore:
Springer Verlag, Singapore, Singapore, 2022
ISBN 10: 9811950725
ISBN 13: 9789811950728
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Da: Grand Eagle Retail, Fairfield, OH, U.S.A.
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Hardcover. Condizione: new. Hardcover. This book elaborately discusses techniques commonly used to improve generalization performance in classification approaches. The contents highlight methods to improve classification performance in numerous case studies: ranging from datasets of UCI repository to predictive maintenance problems and cancer classification problems. The book specifically provides a detailed tutorial on how to approach time-series classification problems and discusses two real time case studies on condition monitoring. In addition to describing the various aspects a data scientist must consider before finalizing their approach to a classification problem and reviewing the state of the art for improving classification generalization performance, it also discusses in detail the authors own contributions to the field, including MVPC - a classifier with very low VC dimension, a graphical indices based framework for reliable predictive maintenance and a novel general-purpose membership functions for Fuzzy Support Vector Machine which provides state of the art performance with noisy datasets, and a novel scheme to introduce deep learning in Fuzzy Rule based classifiers (FRCs). This volume will serve as a useful reference for researchers and students working on machine learning, health monitoring, predictive maintenance, time-series analysis, gene-expression data classification. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9789811950728
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Improving Classifier Generalization: Real-time Machine Learning Based Applications
Editore:
Springer-Nature New York Inc, 2022
ISBN 10: 9811950725
ISBN 13: 9789811950728
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Da: Revaluation Books, Exeter, Regno Unito
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Hardcover. Condizione: Brand New. 189 pages. 9.25x6.10x0.63 inches. In Stock. Codice articolo x-9811950725
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