Statistical Learning Theory and Algorithms

Abstract

This project addressed three fundamental areas in statistical learning theory and algorithms: (1) a practical and theoretically sound method or estimating generalization performance of nonlinear learning systems (Generalized Prediction Error, GPE), (2) a more powerful and efficient class of network architectures (Parameterized Projection Pursuit Regression (P(3)R) networks), and (3) faster real-time learning methods based on asymptotically optimal stochastic gradient search. Three papers were published under this grant. Additionally, a graduate student finished his PhD under research topic Networks with Learned Unit Response Functions .

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Document Details

Document Type
Technical Report
Publication Date
Feb 14, 1993
Accession Number
ADA270209

Entities

People

  • John Moody

Organizations

  • Yale University

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Computer Science
  • Computers
  • Computing System Architectures
  • Information Processing
  • Information Systems
  • Learning
  • Network Architecture
  • Neural Networks
  • Signal Processing
  • Theses

Readers

  • Neural Network Machine Learning.
  • STEM Education
  • Statistical inference.