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

• Main Authors: Yu, Shi (Author), Tranchevent, Léon-Charles (Author), Moor, Bart De (Author), Moreau, Yves (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011.
• Series: Studies in Computational Intelligence, 345
• Subjects: Engineering.; Artificial intelligence.; Bioinformatics.; Computational intelligence.; Computational Intelligence.; Artificial Intelligence (incl. Robotics).; Computational Biology/Bioinformatics.

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...

Table of Contents:

• Introduction
• Rayleigh quotient-type problems in machine learning
• Ln-norm Multiple Kernel Learning and Least Squares Support VectorMachines
• Optimized data fusion for kernel k-means Clustering
• Multi-view text mining for disease gene prioritization and clustering
• Optimized data fusion for k-means Laplacian Clustering
• Weighted Multiple Kernel Canonical Correlation
• Cross-species candidate gene prioritization with MerKator
• Conclusion.