Data Clustering Method for Bayesian Data Reduction

Abstract

This invention is a method of training a mean-field Bayesian data reduction algorithm (BDRA) based classifier which includes using an initial training for determining the best number of levels. The Mean-Field BDRA is then retrained for each point in a target data set and training errors are calculated for each training operation. Cluster candidates are identified as those with multiple points having a common training error. Utilizing these cluster candidates and previously identified clusters as the identified target data, the clusters can be confirmed by comparing a newly calculated training error with the previously calculated common training error for the cluster. The method can be repeated until all cluster candidates are identified and tested.

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Document Details

Document Type
Technical Report
Publication Date
Mar 20, 2006
Accession Number
ADD020266

Entities

People

  • Robert S. Lynch

Organizations

  • United States Department of the Navy

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Clustering
  • Data Mining
  • Data Reduction
  • Data Sets
  • Dimensionality Reduction
  • Equations
  • Feature Selection
  • Four Dimensional
  • Inventions
  • Machine Learning
  • Patents
  • Probability
  • Supervised Machine Learning
  • Test Sets
  • United States

Readers

  • Computational Modeling and Simulation
  • Instructional Design and Training Evaluation.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks