Learning Task Relatedness via Dirichlet Process Priors for Linear Regression Models
نویسندگان
چکیده
In this paper we present a hierarchical model of linear regression functions in the context of multi–task learning. The parameters of the linear model are coupled by a Dirichlet Process (DP) prior, which implies a clustering of related functions for different tasks. To make approximate Bayesian inference under this model we apply the Bayesian Hierarchical Clustering (BHC) algorithm. The experiments are conducted on two real world problems: (i) school exam score prediction and (ii) prediction of ground–motion parameters. In comparison to baseline methods with no shared prior the results show an improved prediction performance when using the hierarchical model.
منابع مشابه
Package ‘ DPpackage ’ March 15 , 2010
Description This package contains functions to perform inference via simulation from the posterior distributions for Bayesian nonparametric and semiparametric models. Although the name of the package was motivated by the Dirichlet Process prior, the package considers and will consider other priors on functional spaces. So far, DPpackage includes models considering Dirichlet Processes, Dependent...
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