Kernel-based data fusion for machine learning. Methods and applications in bioinformatics and text mining.
type: Item
instance of: scholarly article
title: Kernel-based data fusion for machine learning. Methods and applications in bioinformatics and text mining.
zbMATH Open document ID: 1227.68003
DOI: 10.1007/978-3-642-19406-1
author: Shi Yu
author: Léon-Charles Tranchevent
author: B. De Moor
author: Yves Moreau
published in: Studies in Computational Intelligence
publication date: 2011-05-02T00:00:00Z
review text: The book provides an introduction to data fusion problems using support vector machines (SVMs). In the first part, some learning algorithms in statistics and machine learning are theoretically investigated, the authors showing that many of these algorithms can be formulated as a unified mathematical model -- the Rayleigh quotient -- and can be expressed as dual representations on the basis of kernel methods. The second part of the book is devoted to the development of several novel algorithms for supervised/unsupervised learning. In the third part, the proposed algorithms are substantiated in some real bioinformatics and journal scientometrics applications. The book is meant for researchers, scientists and engineers using SVMs, or other statistical learning methods, but it also may be used as a reference material for graduate courses in machine learning and data mining.
reviewed by: Florin Gorunescu
Mathematics Subject Classification ID: 68-02
Mathematics Subject Classification ID: 62-01
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 62P10
Mathematics Subject Classification ID: 62P99
Mathematics Subject Classification ID: 68T10
zbMATH DE Number: 5883025
zbMATH Keywords: machine learning
zbMATH Keywords: data fusion
zbMATH Keywords: Rayleigh quotient
zbMATH Keywords: kernel learning
zbMATH Keywords: k-means clustering
zbMATH Keywords: text mining
zbMATH Keywords: canonical correlation
describes a project that uses: SHOGUN
describes a project that uses: Mosek
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1007/978-3-642-19406-1
OpenAlex ID: W2506890028
description: scientific article; zbMATH DE number 5883025