Items related to Fusion Methods for Unsupervised Learning Ensembles...
Fusion Methods for Unsupervised Learning Ensembles (Studies in Computational Intelligence, 322) - Softcover
Synopsis
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.
"synopsis" may belong to another edition of this title.
From the Back Cover
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 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 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.
"About this title" may belong to another edition of this title.
Other Popular Editions of the Same Title
Search results for Fusion Methods for Unsupervised Learning Ensembles...
Stock Image
Fusion Methods for Unsupervised Learning Ensembles (Studies in Computational Intelligence, 322)
Seller: Lucky's Textbooks, Dallas, TX, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # ABLIING23Mar3113020226669
Quantity: Over 20 available
Stock Image
Fusion Methods for Unsupervised Learning Ensembles (Studies in Computational Intelligence, 322)
Seller: California Books, Miami, FL, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # I-9783642423284
Quantity: Over 20 available
Stock Image
Fusion Methods for Unsupervised Learning Ensembles (Studies in Computational Intelligence, 322)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In. Seller Inventory # ria9783642423284_new
Quantity: Over 20 available
Seller Image
Fusion Methods for Unsupervised Learning Ensembles
Published by
Springer Berlin Heidelberg, Springer Berlin Heidelberg Okt 2014, 2014
ISBN 10: 3642423280
ISBN 13: 9783642423284
New
Taschenbuch
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: 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. Seller Inventory # 9783642423284
Quantity: 2 available
Stock Image
Fusion Methods for Unsupervised Learning Ensembles
Seller: Books Puddle, New York, NY, U.S.A.
Seller rating 4 out of 5 stars
Condition: New. Seller Inventory # 26357304249
Quantity: 4 available
Seller Image
Fusion Methods for Unsupervised Learning Ensembles
Published by
Springer Berlin Heidelberg, 2014
ISBN 10: 3642423280
ISBN 13: 9783642423284
New
Softcover
Seller: moluna, Greven, Germany
Seller rating 5 out of 5 stars
Condition: 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. Seller Inventory # 11800623
Quantity: Over 20 available
Stock Image
Fusion Methods for Unsupervised Learning Ensembles
Print on DemandSeller: Majestic Books, Hounslow, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. Print on Demand. Seller Inventory # 356235366
Quantity: 4 available
Stock Image
Fusion Methods for Unsupervised Learning Ensembles
Print on DemandSeller: Biblios, Frankfurt am main, HESSE, Germany
Seller rating 5 out of 5 stars
Condition: New. PRINT ON DEMAND. Seller Inventory # 18357304243
Quantity: 4 available
Seller Image
Fusion Methods for Unsupervised Learning Ensembles
Published by
Springer Berlin Heidelberg, Springer Berlin Heidelberg Okt 2014, 2014
ISBN 10: 3642423280
ISBN 13: 9783642423284
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: 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. Seller Inventory # 9783642423284
Quantity: 1 available
Seller Image
Fusion Methods for Unsupervised Learning Ensembles
Published by
Springer Berlin Heidelberg, Springer Berlin Heidelberg, 2014
ISBN 10: 3642423280
ISBN 13: 9783642423284
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: 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. Seller Inventory # 9783642423284
Quantity: 1 available