Kernel-based Data Fusion for Machine Learning: Methods and Applications in Bioinformatics and Text Mining

摘要: Data fusion problems arise frequently in many different fields. This book provides a specific introduction to data using support vector machines. In the first part, this begins with brief survey of additive models and Rayleigh quotient objectives machine learning, then introduces kernel as expansion machines dual problem. The second part presents several novel algorithms some real applications supervised unsupervised learning. last substantiates value proposed theories MerKator, an open software identify disease relevant genes based on integration heterogeneous genomic sources multiple species. topics presented are meant for researchers or students who use Several addressed may also be interesting computational biologists want tackle challenges applications. background required reader is good knowledge mining, learning linear algebra.