Use of boosting to improve LVQ ATR classifiers

Su-How Lim; Nasser M. Nasrabadi; Russell M. Mersereau

doi:10.1117/12.461671

5 April 2002 Use of boosting to improve LVQ ATR classifiers

Su-How Lim, Nasser M. Nasrabadi, Russell M. Mersereau

Proceedings Volume 4668, Applications of Artificial Neural Networks in Image Processing VII; (2002) https://doi.org/10.1117/12.461671
Event: Electronic Imaging, 2002, San Jose, California, United States

Abstract

Boosting has emerged as a popular combination technique to refine weak classifiers. Pioneered by Freund and Schapire, numerous variations of the AdaBoost algorithm have emerged, such as Breiman's arc-fs algorithms. The central theme of these methods is the generation of an ensemble of a weak learning algorithm using modified versions of the original training set, with emphasis placed on the more difficult instances. The validation stage then aggregates results from each element of the ensemble using some predetermined rule. In this paper the wavelet decomposition based codebook classifier proposed by Chan et al. is used as the learning algorithm. Starting with the whole training set, modifications to the training set are made at each iteration by resampling the original training data set with replacement. The weights used in the resampling are determined using different algorithms, including AdaBoost and arc-fs. Accuracy of the ensembles generated are then determined using various combination techniques such as simple voting and weighted sum.

Citation Download Citation

Su-How Lim, Nasser M. Nasrabadi, and Russell M. Mersereau "Use of boosting to improve LVQ ATR classifiers", Proc. SPIE 4668, Applications of Artificial Neural Networks in Image Processing VII, (5 April 2002); https://doi.org/10.1117/12.461671

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