Autocorrelation and Relational Learning: Challenges and Opportunities

Jennifer Neville; Özgür Şimşek; David Jensen

Autocorrelation and Relational Learning: Challenges and Opportunities

Jennifer Neville, Özgür Şimşek, David Jensen

Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding

77 Downloads (Pure)

Abstract

Autocorrelation, a common characteristic of many datasets, refers to correlation between values of the same variable on related objects. It violates the critical assumption of in- stance independence that underlies most conventional models. In this paper, we provide an overview of research on autocorrelation in a number of fields with an emphasis on implications for relational learning, and outline a number of challenges and opportunities for model learning and inference.

Original language	English
Title of host publication	ICML 2004 Workshop on Statistical Relational Learning and its Connections to Other Fields
Pages	74–81
Number of pages	8
Publication status	Published - 1 Jan 2004

Access to Document

nsj2004Final published version, 292 KB

Cite this

@inproceedings{20f21792dc08480a9948920a952960ff,

title = "Autocorrelation and Relational Learning: Challenges and Opportunities",

abstract = "Autocorrelation, a common characteristic of many datasets, refers to correlation between values of the same variable on related objects. It violates the critical assumption of in- stance independence that underlies most conventional models. In this paper, we provide an overview of research on autocorrelation in a number of fields with an emphasis on implications for relational learning, and outline a number of challenges and opportunities for model learning and inference.",

author = "Jennifer Neville and {\"O}zg{\"u}r {\c S}im{\c s}ek and David Jensen",

year = "2004",

month = jan,

day = "1",

language = "English",

pages = "74–81",

booktitle = "ICML 2004 Workshop on Statistical Relational Learning and its Connections to Other Fields",

}

TY - GEN

T1 - Autocorrelation and Relational Learning: Challenges and Opportunities

AU - Neville, Jennifer

AU - Şimşek, Özgür

AU - Jensen, David

PY - 2004/1/1

Y1 - 2004/1/1

N2 - Autocorrelation, a common characteristic of many datasets, refers to correlation between values of the same variable on related objects. It violates the critical assumption of in- stance independence that underlies most conventional models. In this paper, we provide an overview of research on autocorrelation in a number of fields with an emphasis on implications for relational learning, and outline a number of challenges and opportunities for model learning and inference.

AB - Autocorrelation, a common characteristic of many datasets, refers to correlation between values of the same variable on related objects. It violates the critical assumption of in- stance independence that underlies most conventional models. In this paper, we provide an overview of research on autocorrelation in a number of fields with an emphasis on implications for relational learning, and outline a number of challenges and opportunities for model learning and inference.

M3 - Chapter in a published conference proceeding

SP - 74

EP - 81

BT - ICML 2004 Workshop on Statistical Relational Learning and its Connections to Other Fields

ER -

Autocorrelation and Relational Learning: Challenges and Opportunities

Abstract

Access to Document

Fingerprint

Cite this