Improving ATR performance by incorporating virtual negative examples
One common problem in learning from examples is the insufficient size of the training set. Many researchers have proposed methods to counteract this shortcoming, such as the noisy interpolation theory, hints, new distance measure (tangent distance), virtual examples, etc. This paper presents the idea of creating virtual negative examples as severe distortions of the known class patterns. Two classifiers are studied, a perceptron and a support vector machine trained to recognize objects in synthetic aperture radar (SAR) images. They utilize the training set (positive examples) to create the discriminant function of each class in the conventional way. On the other hand, the virtual negative examples will help determine the regions where the discriminant function should yield a low value. The experimental results show that incorporating the negative examples improves greatly (up to 50 percent improvement) the confuser rejection rates.