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Improving Classifier Generalization: Real-Time Machine Learning based Applications: 989 (Studies in Computational Intelligence) - Tapa dura
- EditorialSpringer-Verlag GmbH
- Año de publicación2022
- ISBN 10 9811950725
- ISBN 13 9789811950728
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- IdiomaInglés
- Número de edición1
- Número de páginas166
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Improving Classifier Generalization : Real-Time Machine Learning based Applications
Publicado por
Springer Nature Singapore, 2022
ISBN 10: 9811950725
ISBN 13: 9789811950728
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Librería: Buchpark, Trebbin, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: Hervorragend. Zustand: Hervorragend | Seiten: 192 | Sprache: Englisch | Produktart: Bücher. Nº de ref. del artículo: 40310564/1
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Improving Classifier Generalization
Publicado por
Springer Nature Singapore Sep 2022, 2022
ISBN 10: 9811950725
ISBN 13: 9789811950728
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: 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. Nº de ref. del artículo: 9789811950728
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Improving Classifier Generalization
Publicado por
Springer, Berlin|Springer Nature Singapore|Springer, 2022
ISBN 10: 9811950725
ISBN 13: 9789811950728
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Librería: moluna, Greven, Alemania
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Condición: 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. Nº de ref. del artículo: 628808176
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Improving Classifier Generalization : Real-Time Machine Learning based Applications
Publicado por
Springer Nature Singapore, Springer Nature Singapore, 2022
ISBN 10: 9811950725
ISBN 13: 9789811950728
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Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: 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. Nº de ref. del artículo: 9789811950728
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Improving Classifier Generalization (Hardcover)
Publicado por
Springer Verlag, Singapore, Singapore, 2022
ISBN 10: 9811950725
ISBN 13: 9789811950728
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Librería: Grand Eagle Retail, Fairfield, OH, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Hardcover. Condición: 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. Nº de ref. del artículo: 9789811950728
Cantidad disponible: 1 disponibles
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Improving Classifier Generalization: Real-time Machine Learning Based Applications
Publicado por
Springer-Nature New York Inc, 2022
ISBN 10: 9811950725
ISBN 13: 9789811950728
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Librería: Revaluation Books, Exeter, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Hardcover. Condición: Brand New. 189 pages. 9.25x6.10x0.63 inches. In Stock. Nº de ref. del artículo: x-9811950725
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