Learning Statistical Patterns in Relational Data Using Probabilistic Relational Models

Abstract

This report describes techniques for learning probabilistic models of relational data, and using these models to interpret new relational data. This effort focused on developing undirected probabilistic models for representing and learning graph patterns, learning patterns involving links between objects, learning discriminative models for classification in relational data, developing and labeling two real-world relational data sets - one involving web data and the other a social network - and evaluating the performance of our methods on these data sets, and dealing with distributions that are non-uniform, in that different contexts (time periods, organizations) have statistically different properties. The technology developed under this effort was transitioned and is being used under the Perceptive Assistant Program (PAL) at DARPA.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2005
Accession Number
ADA430268

Entities

People

  • Daphne Koller

Organizations

  • Stanford University

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Bayesian Networks
  • Computational Science
  • Computer Science
  • Computer Vision
  • Data Mining
  • Data Sets
  • Information Processing
  • Information Science
  • Linear Programming
  • Machine Learning
  • Network Science
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Relational Database Management Systems
  • Social Networks
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Artificial Intelligence
  • Computer Vision.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks