Kernel-Based Data Fusion for Machine Learning: Methods and Applications in Bioinformatics and Text Mining (Hardcover)

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.