Verwandte Artikel zu Fusion Methods for Unsupervised Learning Ensembles:...
Fusion Methods for Unsupervised Learning Ensembles: 322 (Studies in Computational Intelligence) - Softcover
Inhaltsangabe
The application of a “committee of experts” or ensemble learning to artificial neural networks that apply unsupervised learning techniques is widely considered to enhance the effectiveness of such networks greatly. This book examines the potential of the ensemble meta-algorithm by describing and testing a technique based on the combination of ensembles and statistical PCA that is able to determine the presence of outliers in high-dimensional data sets and to minimize outlier effects in the final results. Its central contribution concerns an algorithm for the ensemble fusion of topology-preserving maps, referred to as Weighted Voting Superposition (WeVoS), which has been devised to improve data exploration by 2-D visualization over multi-dimensional data sets. This generic algorithm is applied in combination with several other models taken from the family of topology preserving maps, such as the SOM, ViSOM, SIM and Max-SIM. A range of quality measures for topology preserving maps that are proposed in the literature are used to validate and compare WeVoS with other algorithms. The experimental results demonstrate that, in the majority of cases, the WeVoS algorithm outperforms earlier map-fusion methods and the simpler versions of the algorithm with which it is compared. All the algorithms are tested in different artificial data sets and in several of the most common machine-learning data sets in order to corroborate their theoretical properties. Moreover, a real-life case-study taken from the food industry demonstrates the practical benefits of their application to more complex problems.
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
Von der hinteren Coverseite
The application of a committee of experts or ensemble learning to artificial neural networksthat apply unsupervised learning techniques is widely considered to enhance the effectivenessof such networks greatly.This book examines the potential of the ensemble meta-algorithm by describing and testing atechnique based on the combination of ensembles and statistical PCA that is able to determinethe presence of outliers in high-dimensional data sets and to minimize outlier effects in the final results.Its central contribution concerns an algorithm for the ensemble fusion of topology-preservingmaps, referred to as Weighted Voting Superposition (WeVoS), which has been devised to improve data exploration by 2-D visualization over multi-dimensional data sets. This generic algorithm is applied in combination with several other models taken from the family of topology preserving maps, such as the SOM, ViSOM, SIM and Max-SIM. A range of quality measures for topologypreserving maps that are proposed in the literature are used to validate and compare WeVoS with other algorithms.The experimental results demonstrate that, in the majority of cases, the WeVoS algorithmoutperforms earlier map-fusion methods and the simpler versions of the algorithm with whichit is compared. All the algorithms are tested in different artificial data sets and in several of the most common machine-learning data sets in order to corroborate their theoretical properties. Moreover, a real-life case-study taken from the food industry demonstrates the practical benefits of their application to more complex problems.
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
EUR 179,21
EUR 28,62 für den Versand von Vereinigtes Königreich nach USA
Versandziele, Kosten & DauerNeu kaufen
Diesen Artikel anzeigen
EUR 109,31
EUR 3,38 für den Versand innerhalb von/der USA
Versandziele, Kosten & DauerWeitere beliebte Ausgaben desselben Titels
Suchergebnisse für Fusion Methods for Unsupervised Learning Ensembles:...
Beispielbild für diese ISBN
Fusion Methods for Unsupervised Learning Ensembles (Studies in Computational Intelligence, 322)
Anbieter: Lucky's Textbooks, Dallas, TX, USA
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. Bestandsnummer des Verkäufers ABLIING23Mar3113020226669
Anzahl: Mehr als 20 verfügbar
Beispielbild für diese ISBN
Fusion Methods for Unsupervised Learning Ensembles (Studies in Computational Intelligence, 322)
Anbieter: California Books, Miami, FL, USA
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. Bestandsnummer des Verkäufers I-9783642423284
Anzahl: Mehr als 20 verfügbar
Beispielbild für diese ISBN
Fusion Methods for Unsupervised Learning Ensembles (Studies in Computational Intelligence, 322)
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. In. Bestandsnummer des Verkäufers ria9783642423284_new
Anzahl: Mehr als 20 verfügbar
Foto des Verkäufers
Fusion Methods for Unsupervised Learning Ensembles
ISBN 10: 3642423280
ISBN 13: 9783642423284
Neu
Taschenbuch
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The application of a 'committee of experts' or ensemble learning to artificial neural networksthat apply unsupervised learning techniques is widely considered to enhance the effectivenessof such networks greatly.This book examines the potential of the ensemble meta-algorithm by describing and testing atechnique based on the combination of ensembles and statistical PCA that is able to determinethe presence of outliers in high-dimensional data sets and to minimize outlier effects in the final results.Its central contribution concerns an algorithm for the ensemble fusion of topology-preservingmaps, referred to as Weighted Voting Superposition (WeVoS), which has been devised to improve data exploration by 2-D visualization over multi-dimensional data sets. This generic algorithm is applied in combination with several other models taken from the family of topology preserving maps, such as the SOM, ViSOM, SIM and Max-SIM. A range of quality measures for topology preserving maps that are proposed in the literature are used to validate and compare WeVoS with other algorithms.The experimental results demonstrate that, in the majority of cases, the WeVoS algorithmoutperforms earlier map-fusion methods and the simpler versions of the algorithm with whichit is compared. All the algorithms are tested in different artificial data sets and in several of the most common machine-learning data sets in order to corroborate their theoretical properties. Moreover, a real-life case-study taken from the food industry demonstrates the practical benefits of their application to more complex problems. 160 pp. Englisch. Bestandsnummer des Verkäufers 9783642423284
Anzahl: 2 verfügbar
Foto des Verkäufers
Fusion Methods for Unsupervised Learning Ensembles
Print-on-DemandAnbieter: moluna, Greven, Deutschland
Verkäuferbewertung 4 von 5 Sternen
Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Recent research in Fusion Methods for Unsupervised Learning Ensembles Examines the potential of the ensemble meta-algorithm Written by leading experts in the fieldRecent research in Fusion Methods for Unsupervised Learning EnsemblesExamines th. Bestandsnummer des Verkäufers 11800623
Anzahl: Mehr als 20 verfügbar
Beispielbild für diese ISBN
Fusion Methods for Unsupervised Learning Ensembles
Anbieter: Books Puddle, New York, NY, USA
Verkäuferbewertung 4 von 5 Sternen
Zustand: New. Bestandsnummer des Verkäufers 26357304249
Anzahl: 4 verfügbar
Beispielbild für diese ISBN
Fusion Methods for Unsupervised Learning Ensembles
Print-on-DemandAnbieter: Majestic Books, Hounslow, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. Print on Demand. Bestandsnummer des Verkäufers 356235366
Anzahl: 4 verfügbar
Beispielbild für diese ISBN
Fusion Methods for Unsupervised Learning Ensembles
Print-on-DemandAnbieter: Biblios, Frankfurt am main, HESSE, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. PRINT ON DEMAND. Bestandsnummer des Verkäufers 18357304243
Anzahl: 4 verfügbar
Foto des Verkäufers
Fusion Methods for Unsupervised Learning Ensembles
ISBN 10: 3642423280
ISBN 13: 9783642423284
Neu
Taschenbuch
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The application of a ¿committee of experts¿ or ensemble learning to artificial neural networksthat apply unsupervised learning techniques is widely considered to enhance the effectivenessof such networks greatly.This book examines the potential of the ensemble meta-algorithm by describing and testing atechnique based on the combination of ensembles and statistical PCA that is able to determinethe presence of outliers in high-dimensional data sets and to minimize outlier effects in the final results.Its central contribution concerns an algorithm for the ensemble fusion of topology-preservingmaps, referred to as Weighted Voting Superposition (WeVoS), which has been devised to improve data exploration by 2-D visualization over multi-dimensional data sets. This generic algorithm is applied in combination with several other models taken from the family of topology preserving maps, such as the SOM, ViSOM, SIM and Max-SIM. A range of quality measures for topology preserving maps that are proposed in the literature are used to validate and compare WeVoS with other algorithms.The experimental results demonstrate that, in the majority of cases, the WeVoS algorithmoutperforms earlier map-fusion methods and the simpler versions of the algorithm with whichit is compared. All the algorithms are tested in different artificial data sets and in several of the most common machine-learning data sets in order to corroborate their theoretical properties. Moreover, a real-life case-study taken from the food industry demonstrates the practical benefits of their application to more complex problems.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 160 pp. Englisch. Bestandsnummer des Verkäufers 9783642423284
Anzahl: 1 verfügbar
Foto des Verkäufers
Fusion Methods for Unsupervised Learning Ensembles
Verlag:
Springer Berlin Heidelberg, Springer Berlin Heidelberg, 2014
ISBN 10: 3642423280
ISBN 13: 9783642423284
Neu
Taschenbuch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - The application of a 'committee of experts' or ensemble learning to artificial neural networksthat apply unsupervised learning techniques is widely considered to enhance the effectivenessof such networks greatly.This book examines the potential of the ensemble meta-algorithm by describing and testing atechnique based on the combination of ensembles and statistical PCA that is able to determinethe presence of outliers in high-dimensional data sets and to minimize outlier effects in the final results.Its central contribution concerns an algorithm for the ensemble fusion of topology-preservingmaps, referred to as Weighted Voting Superposition (WeVoS), which has been devised to improve data exploration by 2-D visualization over multi-dimensional data sets. This generic algorithm is applied in combination with several other models taken from the family of topology preserving maps, such as the SOM, ViSOM, SIM and Max-SIM. A range of quality measures for topology preserving maps that are proposed in the literature are used to validate and compare WeVoS with other algorithms.The experimental results demonstrate that, in the majority of cases, the WeVoS algorithmoutperforms earlier map-fusion methods and the simpler versions of the algorithm with whichit is compared. All the algorithms are tested in different artificial data sets and in several of the most common machine-learning data sets in order to corroborate their theoretical properties. Moreover, a real-life case-study taken from the food industry demonstrates the practical benefits of their application to more complex problems. Bestandsnummer des Verkäufers 9783642423284
Anzahl: 1 verfügbar