By Topic

A self-improving classifier design for high-dimensional data analysis with a limited training data set

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

2 Author(s)
Qiong Jackson ; Sch. of Electr. & Comput. Eng., Purdue Univ., West Lafayette, IN, USA ; Landgrebe, David

In this paper, we propose a self-learning and self-improving adaptive classifier to mitigate the problem of small training sample size that can severely affect the recognition accuracy of classifiers when the dimensionality of the multispectral data is high. This proposed adaptive classifier utilizes classified samples, referred as semi-labeled samples, in addition to the original training samples iteratively. In order to control the influence of semi-labeled samples, the proposed method gives full weight to the training samples and reduced weight to semi-labeled samples. Experimental results show that starting with a small training set this proposed adaptive classifier can improve the classification accuracy as well as representation of estimated statistics to a practically significant extent iteratively

Published in:

Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International  (Volume:1 )

Date of Conference: