Published 2011
by Springer Berlin Heidelberg in Berlin, Heidelberg .
Written in English
Edition Notes
| Statement | by Shi Yu, Léon-Charles Tranchevent, Bart Moor, Yves Moreau |
| Series | Studies in Computational Intelligence -- 345 |
| Contributions | Tranchevent, Léon-Charles, Moor, Bart, Moreau, Yves, SpringerLink (Online service) |
| The Physical Object | |
|---|---|
| Format | [electronic resource] : |
| ID Numbers | |
| Open Library | OL25545477M |
| ISBN 10 | 9783642194054, 9783642194061 |
Data fusion problems arise frequently in many different fields. This book provides a specific introduction to data fusion problems using support vector machines. In the first part, this book begins with a brief survey of additive models and Rayleigh quotient objectives in machine learning, and then introduces kernel fusion as the additive expansion of support vector machines . springer, Data fusion problems arise frequently in many different fields. This book provides a specific introduction to data fusion problems using support vector machines. In the first part, this book begins with a brief survey of additive models and Rayleigh quotient objectives in machine learning, and then introduces kernel fusion as the additive expansion of support vector machines . Kernel-based Data Fusion for Machine Learning: Methods and Applications in Bioinformatics and Text Mining. Data fusion problems arise frequently in many different fields. This book . Data fusion problems arise frequently in many different fields. This book provides a specific introduction to data fusion problems using support vector machines. In the first part, this book begins with a brief survey of additive models and Rayleigh quotient objectives in machine learning, and then introduces kernel fusion .
Book Description. Containing numerous algorithms and major theorems, this step-by-step guide covers the fundamentals of kernel-based learning theory. Including over two hundred Cited by: Using the dual representations, the task of learning with multiple data sources is related to the kernel based data fusion, which has been actively studied in the recent five years. In the second part of the book, we create several novel algorithms for su- pervised learning and unsupervised learning. The research described in this book covers a number of topics which are relevant to supervised and unsupervised learning by kernel-based data fusion. The discussion of these topics . |a Kernel-based data fusion for machine learning: |b methods and applications in bioinformatics and text mining / |c Shi Yu [and others]. |a New York: |b Springer, |c
Kernel-based data fusion for machine learning:based data fusion for machine learning: methods and applications in bioinformatics and text mining Shi Yu Jury: Prof. dr. ir. H. Hens, . Data fusion problems arise frequently in many different fields. This book provides a specific introduction to data fusion problems using support vector machines. In the first Kernel-based Data Fusion for Machine Learning: Methods and Kernel-based Data Fusion for Machine Learning. To tackle the cross-location human activity recognition problem, a fast and simple adaptive mixed and reduced kernel based extreme learning machine (M-RKELM) model has been proposed in Cited by: functions defined on nonvectorial data. We cover a wide range of methods, ranging from binary classifiers to sophisticated methods for estimation with structured data. 1. Introduction. Over the last ten years estimation and learning meth-ods utilizing positive definite kernels have become rather popular, particu-larly in machine learning.
