| 書目名稱 | Fusion Methods for Unsupervised Learning Ensembles |
| 編輯 | Bruno Baruque,Emilio Corchado |
| 視頻video | http://file.papertrans.cn/351/350952/350952.mp4 |
| 概述 | Recent research in Fusion Methods for Unsupervised Learning Ensembles.Examines the potential of the ensemble meta-algorithm.Written by leading experts in the field |
| 叢書名稱 | Studies in Computational Intelligence |
| 圖書封面 |  |
| 描述 | 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-fusi |
| 出版日期 | Book 2011 |
| 關(guān)鍵詞 | Artificial Neural Networks; Computational Intelligence; Ensemble Learning; Fusion Methods; Unsupervised |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-642-16205-3 |
| isbn_softcover | 978-3-642-42328-4 |
| isbn_ebook | 978-3-642-16205-3Series ISSN 1860-949X Series E-ISSN 1860-9503 |
| issn_series | 1860-949X |
| copyright | Springer Berlin Heidelberg 2011 |
|Archiver|手機(jī)版|小黑屋|
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