Hallo! Tracked shipping to Austria with Delivery Duty Paid for just €3.99  

Ship to
Austria
0

My Account

  • argentina
  • chile
  • colombia
  • españa
  • méxico
  • perú
  • estados unidos
  • internacional

menu

Select your country

Americas

Europe

Rest of the world

portada Fusion Methods for Unsupervised Learning Ensembles (Studies in Computational Intelligence, 322)
Type
Physical Book
Publisher
Author
Language
English
Pages
158
Format
Paperback
ISBN13
9783642423284
Edition No.
2011th

Fusion Methods for Unsupervised Learning Ensembles (Studies in Computational Intelligence, 322)

Bruno Baruque (Author) · Springer · Paperback

Fusion Methods for Unsupervised Learning Ensembles (Studies in Computational Intelligence, 322) - Bruno Baruque

Cheaper New Book Imported to Austria
Delivery: 15 Sep - 22 Sep Shipping: 13 to 17 business days.
128,26 €
Faster New Book Imported to Austria
Delivery: 03 Sep - 07 Sep Shipping: 5 to 6 business days.
162,26 €
Import costs and 10% VAT included in the price ✅
128,26 €

Choose the list to add your product or create one New List

Go to My Wishlists
Calculate shipping cost

Synopsis "Fusion Methods for Unsupervised Learning Ensembles (Studies in Computational Intelligence, 322)"

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.

Customers reviews

More customer reviews
  • 0% (0)
  • 0% (0)
  • 0% (0)
  • 0% (0)
  • 0% (0)

Frequently Asked Questions about the Book

Is the book original?

All books in our catalog are Original.

In what language is the book written?

The book is written in English.

What is the format of this book?

The binding of this edition is Paperback.

Questions and Answers about the Book

Do you have a question about the book? Login to be able to add your own question.

Opinions about Bookdelivery

More customer reviews