Cart (Loading....) | Create Account
Close category search window
 

Nosing Around the Neighborhood: A New System Structure and Classification Rule for Recognition in Partially Exposed Environments

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Dasarathy, B.V. ; M&S Computing, Inc., Huntsville, AL 35805.

The scope of the classical k-NN classification techniques is enlarged under this study to cover partially exposed environments. The modified classification system structure required for successful operation in environments, wherein all the inherent pattern classes are not exposed to the system prior to deployment, is developed and illustrated with the aid of a specific classification rule-the neighborhood census rule (NCR). Admittedly, alternative rules can be visualized to fit this modified structure. However, this study concentrates on the use of NCR to bring out the underlying philosophy and develops optimum thresholds for admittance of unknown samples into the set of presently known classes. These thresholds are learned from the available training samples of these classes. This learning represents a new dimensionality of the learning system structure in that estimates of the domains of the known classes are developed in addition to learning of the discrimination among these classes. This facilitates identification of samples belonging to the classes previously unexposed to the recognition system. Experimental results are also presented in support of the proposed concepts and methodology for operation in partially exposed environments.

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:PAMI-2 ,  Issue: 1 )

Date of Publication:

Jan. 1980

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.