Transformative Pattern Learning

Abstract

Statistical analysis of relational data is a fundamental and novel problem in machine learning and data mining. Such analysis constructs useful statistical models from data about complex relationships among people, places, things, and events. Supported by this research contract, we uncovered fundamental challenges of statistical learning and inference in relational data, we designed and implemented new languages for expressing deterministic and probabilistic dependencies in such data, we developed new algorithms for learning probabilistic models, we implemented an open-source system for knowledge discovery in relational data containing over 40,000 lines of code that has been downloaded more than 1000 times, and we evaluated the utility of those algorithms by undertaking large and realistic applications.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2006
Accession Number
ADA455128

Entities

People

  • David Jensen

Organizations

  • University of Massachusetts Amherst

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Algorithms
  • Artificial Intelligence
  • Computer Science
  • Contracts
  • Data Mining
  • Data Science
  • Databases
  • Information Science
  • Language
  • Machine Learning
  • Models
  • Network Science
  • Predictive Modeling
  • Probabilistic Models
  • Statistical Analysis
  • Statistical Inference

Fields of Study

  • Computer science

Readers

  • Database Systems and Applications
  • Distributed Systems and Data Platform Development

Technology Areas

  • AI & ML