Sequential classification into m multivariate populations using the information based on small samples

Nicholas A. Nechval

doi:10.1117/12.24144

1 September 1990 Sequential classification into m multivariate populations using the information based on small samples

Nicholas A. Nechval

Proceedings Volume 1360, Visual Communications and Image Processing '90: Fifth in a Series; (1990) https://doi.org/10.1117/12.24144
Event: Visual Communications and Image Processing '90, 1990, Lausanne, Switzerland

Abstract

In this paper, the classification problem is considered when the alternative distributions have given functional forms but with unspecified parameters. No systematic attempt seems to have been made to offer solutions for small samples. Here a way for eliminating nuisance parameters from the classification problem is proposed, which is based on consideration of a statistic transforming the original observations, the transformation so chosen that the distribution of this statistic does not depend on the nuisance parameters. The price paid for the elimination of the nuisance parameters is the fact that instead of the original n observations we are left with n1 (n1

Citation Download Citation

Nicholas A. Nechval "Sequential classification into m multivariate populations using the information based on small samples", Proc. SPIE 1360, Visual Communications and Image Processing '90: Fifth in a Series, (1 September 1990); https://doi.org/10.1117/12.24144

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available