Traditionally, the performance of deep convolutional neural networks relies on a large number labeled datasets in advance. However, real-world applications, training data are not collected at once, so an algorithm which can deal with continuous incoming is needed. This learning method called incremental learning, whose main problem catastrophic forgetting. Neural network will perform badly old ...

Recently, due to the advent of artificial neural networks and learning vector quantizers, there is a resurgent interest in reexamining the classical techniques of discriminant analysis to suit the new classifier structures. One of the particular problems of interest is minimum error classification in which the misclassification probability is to be minimized based on a given set of training sam...

Quadratic discriminant analysis (QDA) is a widely used statistical tool to classify observations from different multivariate Normal populations. The generalized quadratic (GQDA) classification rule/classifier, which generalizes the QDA and minimum Mahalanobis distance (MMD) classifiers discriminate between populations with underlying elliptically symmetric distributions competes quite favorably...

The K-nearest neighbor (KNN) classifier is one of the simplest and most common classifiers, yet its performance competes with the most complex classifiers in the literature. The core of this classifier depends mainly on measuring the distance or similarity between the tested example and the training examples. This raises a major question about which distance measures to be used for the KNN clas...

A new bound on the minimum distance of q-ary cyclic codes is proposed. It is based on the description by another cyclic code with small minimum distance. The connection to the BCH bound and the Hartmann–Tzeng (HT) bound is formulated explicitly. We show that for many cases our approach improves the HT bound. Furthermore, we refine our bound for several families of cyclic codes. We define syndro...