Neural Network Design and the Complexity of Learning

Abstract

We formalize a notion of learning that characterizes the training of feed-forward networks. In the field of learning theory, it stands as a new model specialized for the type of learning problems that arise in connectionist networks. The formulation is similar to Vallant's {Val84] in that we ask what can be feasibly learned from examples and stored in a particular data structure.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1988
Accession Number
ADA450825

Entities

People

  • J. S. Judd

Organizations

  • California Institute of Technology

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DTIC Thesaurus Topics

  • Abstracts
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  • Digital Information
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Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

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