Classifier performance model for the many-class case

William J. Schonlau

doi:10.1117/12.478501

7 August 2002 Classifier performance model for the many-class case

William J. Schonlau

Proceedings Volume 4728, Signal and Data Processing of Small Targets 2002; (2002) https://doi.org/10.1117/12.478501
Event: AeroSense 2002, 2002, Orlando, FL, United States

Abstract

A model is presented for predicting classification performance for systems having a large population of classes. The cases of large and small training set size for each class are treated separately. A method is proposed for measuring classification performance as the mean ranking statistic (rho) _E which is derived from the average information content h_E of the system feature vector, which is in turn derived from the system covariance matrices ((Sigma) _W, (Sigma) _B). This method for predicting (rho) _E is applied to the large training set case (case 1), explaining why performance is not compromised but improved by adding noisy features. The method is extended for predicting performance in the more difficult small training set case (case 2), explaining why performance may be compromised by the addition of noisy features in that situation.

Citation Download Citation

William J. Schonlau "Classifier performance model for the many-class case", Proc. SPIE 4728, Signal and Data Processing of Small Targets 2002, (7 August 2002); https://doi.org/10.1117/12.478501

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