Neural Network Exploration Using Optional Experiment Design,

Abstract

We consider the question 'How should one act when the only goal is to learn as much as possible?' Building on the theoretical results of Fedorov (1972) and MacKay (1992), we apply techniques from Optimal Experiment Design (OED) to guide the query/action selection of a neural network learner. We demonstrate that these techniques allow the learner to minimize its generalization error by exploring its domain efficiently and completely. We conclude that, while not a panacea, OED-based query/action has much to offer, especially in domains where its high computational costs can be tolerated.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1994
Accession Number
ADA290058

Entities

People

  • David Cohn

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Autonomy
  • C4I
  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Bayesian Networks
  • Cognitive Science
  • Computational Complexity
  • Computational Science
  • Computer Science
  • Identification
  • Information Processing
  • Information Science
  • Information Systems
  • Machine Learning
  • Neural Networks
  • Simulators
  • Systems Engineering

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

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