Fusion Methods for Unsupervised Learning Ensembles Bruno Baruque (u. a.) Buch Studies in Computational Intelligence Englisch 2010

*- gebunden oder broschiert*

2010, ISBN: 9783642162046

[ED: Gebunden], [PU: Springer Berlin], 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., DE, [SC: 0.00], Neuware, gewerbliches Angebot, 141, [GW: 442g], Sofortüberweisung, PayPal, Banküberweisung

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Fusion Methods for Unsupervised Learning Ensembles Bruno Baruque (u. a.) Buch Studies in Computational Intelligence Englisch 2010

*- gebunden oder broschiert*

2010, ISBN: 9783642162046

[ED: Gebunden], [PU: Springer Berlin], 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., DE, [SC: 0.00], Neuware, gewerbliches Angebot, 141, [GW: 442g], Sofortüberweisung, PayPal, Banküberweisung

booklooker.de |

2010, ISBN: 9783642162046

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. Buch (fremdspr.) Bruno Baruque gebundene Ausgabe, Springer Berlin, 23.11.2010, Springer Berlin, 2010

Thalia.de Nr. 23957208. Versandkosten:, Sofort lieferbar, DE. (EUR 0.00) Details... |

ISBN: 9783642162046

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. Bücher, Hörbücher & Kalender / Bücher / Sachbuch / Herstellung & Technik, [PU: Springer, Berlin/Heidelberg/New York, NY]

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2010, ISBN: 9783642162046

Buch, Hardcover, 2011, [PU: Springer Berlin], Springer Berlin, 2010

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# Fusion Methods for Unsupervised Learning Ensembles Bruno Baruque (u. a.) Buch Studies in Computational Intelligence Englisch 2010* - gebunden oder broschiert*

2010, ISBN: 9783642162046

[ED: Gebunden], [PU: Springer Berlin], The application of a "committee of experts" or ensemble learning to artificial neural networksthat apply unsupervised learning techniques is widely … Mehr…

## Baruque, Bruno:

Fusion Methods for Unsupervised Learning Ensembles Bruno Baruque (u. a.) Buch Studies in Computational Intelligence Englisch 2010*- gebunden oder broschiert*

2010, ISBN: 9783642162046

[ED: Gebunden], [PU: Springer Berlin], The application of a "committee of experts" or ensemble learning to artificial neural networksthat apply unsupervised learning techniques is widely … Mehr…

2010

## ISBN: 9783642162046

The application of a “committee of experts” or ensemble learning to artificial neural networks that apply unsupervised learning techniques is widely considered to enh… Mehr…

ISBN: 9783642162046

The application of a "committee of experts" or ensemble learning to artificial neural networksthat apply unsupervised learning techniques is widely considered to enhance the effectiveness… Mehr…

2010, ISBN: 9783642162046

Buch, Hardcover, 2011, [PU: Springer Berlin], Springer Berlin, 2010

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** Detailangaben zum Buch - Fusion Methods for Unsupervised Learning Ensembles**

EAN (ISBN-13): 9783642162046

ISBN (ISBN-10): 3642162045

Gebundene Ausgabe

Erscheinungsjahr: 2010

Herausgeber: Springer Berlin

141 Seiten

Gewicht: 0,387 kg

Sprache: eng/Englisch

Buch in der Datenbank seit 2008-09-18T06:44:08+02:00 (Berlin)

Detailseite zuletzt geändert am 2021-10-13T16:48:54+02:00 (Berlin)

ISBN/EAN: 9783642162046

ISBN - alternative Schreibweisen:

3-642-16204-5, 978-3-642-16204-6

### Daten vom Verlag:

Autor/in: Bruno Baruque

Titel: Studies in Computational Intelligence; Fusion Methods for Unsupervised Learning Ensembles

Verlag: Springer; Springer Berlin

141 Seiten

Erscheinungsjahr: 2010-11-23

Berlin; Heidelberg; DE

Gedruckt / Hergestellt in Niederlande.

Gewicht: 0,442 kg

Sprache: Englisch

128,39 € (DE)

131,99 € (AT)

141,50 CHF (CH)

POD

BB; Book; Hardcover, Softcover / Technik/Allgemeines, Lexika; Künstliche Intelligenz; Verstehen; Informatik; Artificial Neural Networks; Computational Intelligence; Ensemble Learning; Fusion Methods; Unsupervised Learning; B; Computational Intelligence; Artificial Intelligence; Computational Intelligence; Artificial Intelligence; Engineering; Künstliche Intelligenz; BC; EA

1 Introduction.- 2 Modelling Human Learning: Artificial Neural Networks.- 3 The Committee of Experts Approach: Ensemble Learning.- 4 Use of Ensembles for Outlier Overcoming.- 5 Ensembles of Topology Preserving Maps.- 6 A Novel Fusion Algorithm for Topology-Preserving Maps.-7 Conclusions.### Weitere, andere Bücher, die diesem Buch sehr ähnlich sein könnten:

### Neuestes ähnliches Buch:

*9783642162053 Fusion Methods for Unsupervised Learning Ensembles (Bruno Baruque)*

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