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Fusion Methods for Unsupervised Learning Ensembles by Bruno Baruque Hardcover | Indigo Chapters - libro nuevo
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… Más…
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… Más…
2010
ISBN: 9783642162046
Pasta dura
[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… Más…
2010, ISBN: 3642162045
2011 Gebundene Ausgabe Intelligenz / Künstliche Intelligenz, KI, Künstliche Intelligenz - AI, ArtificialNeuralNetworks; ComputationalIntelligence; ensemblelearning; FusionMethods; unsup… Más…
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… Más…
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Detalles del libro - Fusion Methods for Unsupervised Learning Ensembles
EAN (ISBN-13): 9783642162046
ISBN (ISBN-10): 3642162045
Tapa dura
Tapa blanda
Año de publicación: 2010
Editorial: Springer Berlin Heidelberg
141 Páginas
Peso: 0,387 kg
Idioma: eng/Englisch
Libro en la base de datos desde 2008-09-17T23:44:08-05:00 (Mexico City)
Página de detalles modificada por última vez el 2023-09-07T07:53:13-06:00 (Mexico City)
ISBN/EAN: 9783642162046
ISBN - escritura alterna:
3-642-16204-5, 978-3-642-16204-6
Mode alterno de escritura y términos de búsqueda relacionados:
Autor del libro: emilio, baru, bruno, food experts
Título del libro: fusion, ensembles, bruno buch, intelligence studies, unsupervised learning
Datos del la editorial
Autor: Bruno Baruque
Título: Studies in Computational Intelligence; Fusion Methods for Unsupervised Learning Ensembles
Editorial: Springer; Springer Berlin
141 Páginas
Año de publicación: 2010-11-23
Berlin; Heidelberg; DE
Impreso en
Idioma: Inglés
106,99 € (DE)
109,99 € (AT)
118,00 CHF (CH)
POD
XVII, 141 p.
BB; Hardcover, Softcover / Technik/Allgemeines, Lexika; Künstliche Intelligenz; Verstehen; Informatik; Artificial Neural Networks; Computational Intelligence; Ensemble Learning; Fusion Methods; Unsupervised Learning; Computational Intelligence; Artificial Intelligence; BC
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.Recent research in Fusion Methods for Unsupervised Learning Ensembles Examines the potential of the ensemble meta-algorithm Written by leading experts in the field
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