The Modified Quadratic Discriminant Function was first proposed by Kimura et al to improve the performance of Quadratic Discriminant Function, which can be seen as a dot-product method by eigen-decompostion of the covariance matrix of each class. Therefore, it is possible to expand MQDF to high dimension space by kernel trick. This paper presents a new kernel-based method to pattern recognition...

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...

Discriminant analysis, including linear discriminant analysis (LDA) and quadratic (QDA), is a popular approach to classification problems. It well known that LDA suboptimal analyze heteroscedastic data, for which QDA would be an ideal tool. However, less helpful when the number of features in data set moderate or high, its variants often perform better due their robustness against dimensionalit...

-In this paper, the authors combine two algorithms for application to the recognition of unconstrained isolated handwritten numerals. The first algorithm employs a modified quadratic discriminant function utilizing direction sensitive spatial features of the numeral image. The second algorithm utilizes features derived from the profile of the character in a structural configuration to recognize...