CHEBYSHEV BOUNDS FOR RISKS AND ERROR PROBABILITIES IN SOME CLASSIFICATION PROBLEMS,

Abstract

It is often the case that a random variable Y characterizes an item which is to be classified as belonging to one of several classes. When Y is expensive or impossible to observe, difficulties of classification arise and it may be desirable or necessary to classify the item on the basis of an auxiliary random variable X which is correlated with Y and may be regarded as a measure of Y. Of course, misclassification is a possibility. We assume that the loss suffered in such an event is determined by X and Y. When the joint distribution of X and Y is unknown, but only means, variances and covariance are known, the expected loss (risk) cannot be precisely computed. However, Chebyshev-type bounds for the risk can often be obtained for a given loss function. In this paper, several examples are presented which not only serve as illustrations, but which also are important in their own right. For certain kinds of classification procedures, bounds are also obtained for probabilities of misclassification. (Author)

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1969
Accession Number
AD0684495

Entities

People

  • Albert W. Marshall
  • Ingram Olkin

Organizations

  • Boeing

Tags

DTIC Thesaurus Topics

  • Classification
  • Covariance
  • Data Science
  • Information Science
  • Mathematics
  • Probability
  • Probability Distributions
  • Random Variables
  • Stochastic Processes

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.
  • Statistical inference.

Technology Areas

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