2005 Special Issue: A hierarchical classifier using new support vector machines for automatic target recognition

Authors:
David Casasent;Yu-Chiang Wang
Affiliations:
Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA;Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Venue:
Neural Networks - 2005 Special issue: IJCNN 2005
Year:
2005

Citing 3
Cited 6

Classification by pairwise coupling

NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Efficient classification for multiclass problems using modular neural networks

IEEE Transactions on Neural Networks

2008 Special Issue: New support vector-based design method for binary hierarchical classifiers for multi-class classification problems

Neural Networks
A support vector hierarchical method for multi-class classification and rejection

IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
New clustering algorithms for the support vector machine based hierarchical classification

Pattern Recognition Letters
Using AdaBoost classifiers in a hierarchical framework for classifying surface images of marble slabs

Expert Systems with Applications: An International Journal
Novel multiclass classification for home-based diagnosis of sleep apnea hypopnea syndrome

Expert Systems with Applications: An International Journal
Hierarchical classifier: Based on neural networks searching tree with iterative traversal and stop criterion

Optical Memory and Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

A binary hierarchical classifier is proposed for automatic target recognition. We also require rejection of non-object (non-target) inputs, which are not seen during training or validation, thus producing a very difficult problem. The SVRDM (support vector representation and discrimination machine) classifier is used at each node in the hierarchy, since it offers good generalization and rejection ability. Using this hierarchical SVRDM classifier with magnitude Fourier transform (|FT|) features, which provide shift-invariance, initial test results on infra-red (IR) data are excellent.