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 effectivenes… Mehr…

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. | Fusion Methods for Unsupervised Learning Ensembles by Bruno Baruque Hardcover | Indigo Chapters Books > Science & Nature > Science > Engineering P10117, Bruno Baruque<

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 effectivenes… Mehr…

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., Springer<

2010, ISBN: 9783642162046

Gebundene Ausgabe

[ED: Gebunden], [PU: Springer Berlin Heidelberg], Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Recent research in Fusion Methods for Un… Mehr…

[ED: Gebunden], [PU: Springer Berlin Heidelberg], 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 fi, DE, [SC: 0.00], Neuware, gewerbliches Angebot, Hardcover, 141, [GW: 442g], 1. Auflage, Banküberweisung, PayPal<

2010, ISBN: 3642162045

2011 Gebundene Ausgabe Intelligenz / Künstliche Intelligenz, KI, Künstliche Intelligenz - AI, ArtificialNeuralNetworks; ComputationalIntelligence; ensemblelearning; FusionMethods; unsup… Mehr…

2011 Gebundene Ausgabe Intelligenz / Künstliche Intelligenz, KI, Künstliche Intelligenz - AI, ArtificialNeuralNetworks; ComputationalIntelligence; ensemblelearning; FusionMethods; unsupervisedlearning, mit Schutzumschlag 11, [PU:Springer Berlin Heidelberg; Springer Berlin]<

2011, ISBN: 9783642162046

Fusion Methods for Unsupervised Learning Ensembles ab 128.49 € als gebundene Ausgabe: Auflage 2011. Aus dem Bereich: Bücher, Wissenschaft, Technik, Medien > Bücher nein Buch (gebunden) Ha… Mehr…

Fusion Methods for Unsupervised Learning Ensembles ab 128.49 € als gebundene Ausgabe: Auflage 2011. Aus dem Bereich: Bücher, Wissenschaft, Technik, Medien > Bücher nein Buch (gebunden) Hardcover;Naturwissenschaften, Medizin, Informatik, Technik;Allgemeines, Lexika, Springer Berlin Heidelberg<