| 书目名称 | Kernel-based Data Fusion for Machine Learning |
| 副标题 | Methods and Applicat |
| 编辑 | Shi Yu,Léon-Charles Tranchevent,Yves Moreau |
| 视频video | |
| 概述 | Recent research on Kernel-based Data Fusion for Machine Learning.Presents methods and applications in bioinformatics and text mining.Written by leading experts in the field |
| 丛书名称 | Studies in Computational Intelligence |
| 图书封面 |  |
| 描述 | .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 in the dual problem. The second part presents several novel kernel fusion algorithms and some real applications in supervised and unsupervised learning. The last part of the book substantiates the value of the proposed theories and algorithms in MerKator, an open software to identify disease relevant genes based on the integration of heterogeneous genomic data sources in multiple species. .The topics presented in this book are meant for researchers or students who use support vector machines. Several topics addressed in the book may also be interesting to computational biologists who want to tackle data fusion challenges in real applications. The background required of the reader is a good knowledge of data mining, machine learning and linear algebra.. . |
| 出版日期 | Book 2011 |
| 关键词 | Bioinformatics; Computational Intelligence; Data Fusion; Kernel Method; Text Mining |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-642-19406-1 |
| isbn_softcover | 978-3-642-26751-2 |
| isbn_ebook | 978-3-642-19406-1Series ISSN 1860-949X Series E-ISSN 1860-9503 |
| issn_series | 1860-949X |
| copyright | Springer Berlin Heidelberg 2011 |
发表于 2025-3-21 18:14:56
