Download Fusion Methods for Unsupervised Learning Ensembles PDF Free - Full Version
Download Fusion Methods for Unsupervised Learning Ensembles by Bruno Baruque, Emilio Corchado (auth.) in PDF format completely FREE. No registration required, no payment needed. Get instant access to this valuable resource on PDFdrive.to!
About Fusion Methods for Unsupervised Learning Ensembles
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.
Detailed Information
| Author: | Bruno Baruque, Emilio Corchado (auth.) |
|---|---|
| Publication Year: | 2011 |
| ISBN: | 9783642162046 |
| Pages: | 158 |
| Language: | English |
| File Size: | 3.304 |
| Format: | |
| Price: | FREE |
Safe & Secure Download - No registration required
Why Choose PDFdrive for Your Free Fusion Methods for Unsupervised Learning Ensembles Download?
- 100% Free: No hidden fees or subscriptions required for one book every day.
- No Registration: Immediate access is available without creating accounts for one book every day.
- Safe and Secure: Clean downloads without malware or viruses
- Multiple Formats: PDF, MOBI, Mpub,... optimized for all devices
- Educational Resource: Supporting knowledge sharing and learning
Frequently Asked Questions
Is it really free to download Fusion Methods for Unsupervised Learning Ensembles PDF?
Yes, on https://PDFdrive.to you can download Fusion Methods for Unsupervised Learning Ensembles by Bruno Baruque, Emilio Corchado (auth.) completely free. We don't require any payment, subscription, or registration to access this PDF file. For 3 books every day.
How can I read Fusion Methods for Unsupervised Learning Ensembles on my mobile device?
After downloading Fusion Methods for Unsupervised Learning Ensembles PDF, you can open it with any PDF reader app on your phone or tablet. We recommend using Adobe Acrobat Reader, Apple Books, or Google Play Books for the best reading experience.
Is this the full version of Fusion Methods for Unsupervised Learning Ensembles?
Yes, this is the complete PDF version of Fusion Methods for Unsupervised Learning Ensembles by Bruno Baruque, Emilio Corchado (auth.). You will be able to read the entire content as in the printed version without missing any pages.
Is it legal to download Fusion Methods for Unsupervised Learning Ensembles PDF for free?
https://PDFdrive.to provides links to free educational resources available online. We do not store any files on our servers. Please be aware of copyright laws in your country before downloading.
The materials shared are intended for research, educational, and personal use in accordance with fair use principles.
