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 instance 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.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2004
Accession Number
ADA472226

Entities

People

  • David Jensen
  • Jennifer Neville
  • Ozgur Simsek

Organizations

  • University of Massachusetts Amherst

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Autocorrelation
  • Bayesian Networks
  • Computational Science
  • Data Mining
  • Data Science
  • Data Sets
  • Estimators
  • Feature Selection
  • Information Science
  • Learning
  • Machine Learning
  • Monte Carlo Method
  • Probabilistic Models
  • Relational Database Management Systems
  • Social Networks
  • Statistical Analysis
  • Statistics

Fields of Study

  • Computer science

Readers

  • Approximation Theory.
  • Economics
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy