High-Dimensional Sparse Structured Input-Output Models, with Applications to GWAS

This chapter contains sections titled: 1 Problem Setup and Notations, 2 Structured Penalty on the Inputs, 3 Optimization Algorithms, 4 Comparison of Optimization Procedures, 5 Structured Output Regression for Correlated Phenome Association, 6 Structured Input Regression for Correlated Genome Associa...

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Bibliographic Details
Published inPractical Applications of Sparse Modeling
Format Book Chapter
LanguageEnglish
Published United States MIT Press 2014
The MIT Press
SeriesNeural Information Processing series
Subjects
Computer Science
Machine learning
Machine Learning & Neural Networks
Neuroscience
Online AccessGet full text
ISBN0262027720
9780262027724
DOI10.7551/mitpress/9333.003.0005

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Summary:This chapter contains sections titled: 1 Problem Setup and Notations, 2 Structured Penalty on the Inputs, 3 Optimization Algorithms, 4 Comparison of Optimization Procedures, 5 Structured Output Regression for Correlated Phenome Association, 6 Structured Input Regression for Correlated Genome Association, 7 Structured Input-Output Regression for Genome-Phenome Association, 8 Summary, Note, References
ISBN:0262027720
9780262027724
DOI:10.7551/mitpress/9333.003.0005