Fusion methods for unsupervised learning ensembles.
type: Item
instance of: scholarly article
title: Fusion methods for unsupervised learning ensembles.
zbMATH Open document ID: 1221.68189
DOI: 10.1007/978-3-642-16205-3
author: Bruno Baruque
author: Emilio Corchado
published in: Studies in Computational Intelligence
publication date: 2010-10-08T00:00:00Z
full work available at URL: http://hdl.handle.net/10366/135212
review text: This book is a monograph on fusion methods used in unsupervised learning combined with ensemble learning. It covers neural nets with their learning strategies, especially training of topology preserving models. Chapter 3 discusses ensemble learning, including the most popular techniques of bagging, boosting, and mixture of experts. Chapter 4 presents the use of ensembles and unsupervised learning for outlier overcoming by generating graphical representations that are inspected by a naked eye. This way the user may recognize the most interesting clusters of data. Such projection methods are based on principal component analysis. The outliers may be recognized by using this technique combined with ensemble learning. Chapter 5 discusses combination of ensembles of topology-preserving maps known as fusion techniques. Chapter 6 presents the main contribution of the book: an improved version of the fusion algorithm. The discussed methodology was validated using many data sets, some artificial, some real-life.
reviewed by: Jerzy W. Grzymala-Busse
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 68-02
zbMATH DE Number: 5797947
zbMATH Keywords: ensemble learning
zbMATH Keywords: neural networks
zbMATH Keywords: unsupervised learning
MaRDI profile type: Publication
OpenAlex ID: W2399247639
description: scientific article; zbMATH DE number 5797947