Active Learning with Statistical Models.

Abstract

For many types of learners one can compute the statistically optimal' way to select data. We review how these techniques have been used with feedforward neural networks. We then show how the same principles may be used to select data for two alternative, statistically-based learning architectures: mixtures of Gaussians and locally weighted regression. While the techniques for neural networks are expensive and approximate, the techniques for mixtures of Gaussians and locally weighted regression are both efficient and accurate.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1995
Accession Number
ADA295617

Entities

People

  • David A. Cohn
  • Michael I. Jordan
  • Zoubin Ghahramani

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Cognitive Science
  • Computer Science
  • Data Science
  • Data Sets
  • Information Processing
  • Information Science
  • Information Systems
  • Learning
  • Machine Learning
  • Network Science
  • Neural Networks

Fields of Study

  • Computer science

Readers

  • Approximation Theory.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks