Goals Versus Algorithms

Abstract

Computational methods in statistics often are defined through an algorithm. It is argued that a precise finite sample specification of the goals to be achieved by the algorithm is at least equally important. The issues are discussed in the special case of Projection Pursuit Regression. An interesting initial result of this work, which is still in progress, is that the Friedman- Stuetzle algorithm appears to be systematically biased toward overfitting.

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Document Details

Document Type
Technical Report
Publication Date
Jun 30, 1992
Accession Number
ADA256106

Entities

People

  • Peter J. Huber

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Case Studies
  • Computational Science
  • Computations
  • Convergence
  • Low Noise
  • Mathematics
  • Noise
  • Observation
  • Random Variables
  • Residuals
  • Specifications
  • Standards
  • Statistical Algorithms
  • Statistics
  • Validation

Readers

  • Neural Network Machine Learning.
  • Regression Analysis.
  • Systems Analysis and Design