Articles liés à Improving Classifier Generalization: Real-time Machine...
Improving Classifier Generalization: Real-time Machine Learning Based Applications - Couverture rigide
- ÉditeurSpringer Verlag, Singapore
- Date d'édition2022
- ISBN 10 9811950725
- ISBN 13 9789811950728
- ReliureRelié
- Langueanglais
- Numéro d'édition1
- Nombre de pages166
Acheter neuf
Afficher cet article
EUR 160,49
EUR 23 expédition depuis Allemagne vers Etats-Unis
Destinations, frais et délaisAutres éditions populaires du même titre
Résultats de recherche pour Improving Classifier Generalization: Real-time Machine...
Image d'archives
Improving Classifier Generalization : Real-Time Machine Learning based Applications
Edité par
Springer Nature Singapore, 2022
ISBN 10 : 9811950725
ISBN 13 : 9789811950728
Ancien ou d'occasion
Couverture rigide
Vendeur : Buchpark, Trebbin, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Etat : Hervorragend. Zustand: Hervorragend | Seiten: 192 | Sprache: Englisch | Produktart: Bücher. N° de réf. du vendeur 40310564/1
Quantité disponible : 2 disponible(s)
Image fournie par le vendeur
Improving Classifier Generalization
Edité par
Springer Nature Singapore Sep 2022, 2022
ISBN 10 : 9811950725
ISBN 13 : 9789811950728
Neuf
Couverture rigide
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : 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. N° de réf. du vendeur 9789811950728
Quantité disponible : 2 disponible(s)
Image fournie par le vendeur
Improving Classifier Generalization
Edité par
Springer, Berlin|Springer Nature Singapore|Springer, 2022
ISBN 10 : 9811950725
ISBN 13 : 9789811950728
Neuf
Couverture rigide
Vendeur : moluna, Greven, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Etat : 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. N° de réf. du vendeur 628808176
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
Improving Classifier Generalization : Real-Time Machine Learning based Applications
Edité par
Springer Nature Singapore, Springer Nature Singapore, 2022
ISBN 10 : 9811950725
ISBN 13 : 9789811950728
Neuf
Couverture rigide
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : 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. N° de réf. du vendeur 9789811950728
Quantité disponible : 1 disponible(s)
Image d'archives
Improving Classifier Generalization (Hardcover)
Edité par
Springer Verlag, Singapore, Singapore, 2022
ISBN 10 : 9811950725
ISBN 13 : 9789811950728
Neuf
Couverture rigide
Edition originale
Vendeur : Grand Eagle Retail, Fairfield, OH, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Hardcover. Etat : 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. N° de réf. du vendeur 9789811950728
Quantité disponible : 1 disponible(s)
Image d'archives
Improving Classifier Generalization: Real-time Machine Learning Based Applications
Edité par
Springer-Nature New York Inc, 2022
ISBN 10 : 9811950725
ISBN 13 : 9789811950728
Neuf
Couverture rigide
Vendeur : Revaluation Books, Exeter, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Hardcover. Etat : Brand New. 189 pages. 9.25x6.10x0.63 inches. In Stock. N° de réf. du vendeur x-9811950725
Quantité disponible : 2 disponible(s)