{
"item_title" : "Kernel-Based Data Fusion for Machine Learning",
"item_author" : [" Shi Yu", "Léon-Charles Tranchevent", "Bart Moor "],
"item_description" : "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.",
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Kernel-Based Data Fusion for Machine Learning : Methods and Applications in Bioinformatics and Text Mining
Overview
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.
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Details
- ISBN-13: 9783642267512
- ISBN-10: 3642267513
- Publisher: Springer
- Publish Date: April 2013
- Dimensions: 9.15 x 6.08 x 0.52 inches
- Shipping Weight: 0.72 pounds
- Page Count: 214
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