Learning with Dependencies between Several Response Variables
published: Aug. 26, 2009, recorded: June 2009, views: 4264
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We analyze situations where modeling several response variables for a given input improves the prediction accuracy for each individual response variable. Interestingly, this setting has appeared in different context and a number of different but related approaches have been proposed. In all these approaches some assumptions about the dependency structure between the response variables is made.
Here is a small selection of labels describing relevant work: multitask learning, multi-class classification, multi-label prediction, hierarchical Bayes, inductive transfer learning, hierarchical linear models, mixed effect models, partial least squares, canonical correlation analysis, maximal covariance regression, multivariate regression, structured prediction, relational learning, ...
The large number of approaches is confusing for the novice, and often even for the expert. In this tutorial we systematically introduce some of the major approaches and describe them from a common viewpoint.
Download slides: icml09_tresp_ldb.pdf (1.1 MB)
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