Fusion Methods for Unsupervised Learning Ensembles: 322 - Brossura

Baruque, Bruno

 
9783642423284: Fusion Methods for Unsupervised Learning Ensembles: 322
Brossura
ISBN 10: 3642423280 ISBN 13: 9783642423284
Casa editrice: Springer, 2014

Sinossi

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.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Dalla quarta di copertina

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.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Editore
Springer
Data di pubblicazione
2014
Lingua
Inglese
ISBN 10 
3642423280
ISBN 13 
9783642423284
Rilegatura
Copertina flessibile
Numero di pagine
160
Contatto del produttore
ProductSafety@springernature.com
ProductSafety@springernature.com

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Darmstadt
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Germania

Altre edizioni note dello stesso titolo

9783642162046: Fusion Methods for Unsupervised Learning Ensembles: 322

Edizione in evidenza

ISBN 10:  3642162045 ISBN 13:  9783642162046
Casa editrice: Springer-Nature New York Inc, 2010
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