Distributed regression for heterogeneous data sets

Michael Madden

Distributed regression for heterogeneous data sets

Computer Science

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

Abstract

Existing meta-learning based distributed data mining approaches do not explicitly address context heterogeneity across individual sites. This limitation constrains their applications where distributed data are not identically and independently distributed. Modeling heterogeneously distributed data with hierarchical models, this paper extends the traditional meta-learning techniques so that they, can be successfully used in distributed scenarios with context heterogeneity.

Original language	English (Ireland)
Title of host publication	ADVANCES IN INTELLIGENT DATA ANALYSIS V
Publisher	SPRINGER-VERLAG BERLIN
Number of pages	9
Volume	2810
ISBN (Electronic)	0302-9743
ISBN (Print)	0302-9743
Publication status	Published - 1 Jan 2003

Authors (Note for portal: view the doc link for the full list of authors)

Authors
Xing, Y;Madden, MG;Duggan, J;Lyons, GJ

Cite this

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title = "Distributed regression for heterogeneous data sets",

abstract = "Existing meta-learning based distributed data mining approaches do not explicitly address context heterogeneity across individual sites. This limitation constrains their applications where distributed data are not identically and independently distributed. Modeling heterogeneously distributed data with hierarchical models, this paper extends the traditional meta-learning techniques so that they, can be successfully used in distributed scenarios with context heterogeneity.",

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N2 - Existing meta-learning based distributed data mining approaches do not explicitly address context heterogeneity across individual sites. This limitation constrains their applications where distributed data are not identically and independently distributed. Modeling heterogeneously distributed data with hierarchical models, this paper extends the traditional meta-learning techniques so that they, can be successfully used in distributed scenarios with context heterogeneity.

AB - Existing meta-learning based distributed data mining approaches do not explicitly address context heterogeneity across individual sites. This limitation constrains their applications where distributed data are not identically and independently distributed. Modeling heterogeneously distributed data with hierarchical models, this paper extends the traditional meta-learning techniques so that they, can be successfully used in distributed scenarios with context heterogeneity.

M3 - Conference Publication

SN - 0302-9743

VL - 2810

BT - ADVANCES IN INTELLIGENT DATA ANALYSIS V

PB - SPRINGER-VERLAG BERLIN

ER -

Distributed regression for heterogeneous data sets

Abstract

Authors (Note for portal: view the doc link for the full list of authors)

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Cite this