A Differential Theory of Learning for Efficient Statistical Pattern Recognition

Abstract

Probabilistic learning strategies currently use are inefficient, requiring high classifier complexity and large training samples. In this report, we introduce and analyze an asymptotically efficient differential learning strategy. It guarantees the best generalization allowed by the chosen classifier paradigm. Differential learning also requires the classifier with minimal complexity. The theory is demonstrated in several real-world machine learning/pattern recognition tasks. Learning, Pattern recognition, Classification, Neural networks.

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Document Details

Document Type
Technical Report
Publication Date
Dec 15, 1993
Accession Number
ADA277313

Entities

People

  • B. V. Kumar
  • John Hampshire

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy
  • C4I
  • Energy and Power Technologies
  • Space

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Computational Science
  • Computer Programming
  • Computer Programs
  • Computer Science
  • Computers
  • Data Compression
  • Dimensionality Reduction
  • Information Processing
  • Information Science
  • Information Theory
  • Machine Learning
  • Network Science
  • Neural Networks
  • Pattern Recognition
  • Probabilistic Models
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Control Systems Engineering.
  • Neural Network Machine Learning.

Technology Areas

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