Quadratic discriminant analysis is a common tool for classification, but estimation of the Gaussian parameters can be ill-posed. This paper contains theoretical and algorithmic contributions to Bayesian estimation for quadratic discriminant analysis. A distribution-based Bayesian classifier

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Sufficient dimension reduction methods provide effective ways to visualize discriminant anal-ysis problems. For example, Cook and Yin (2001) showed that the dimension reduction method of sliced average variance estimation (save) identifies variates that are equivalent to a quadratic discriminant

Chilean wine varietal classification using quadratic Fisher

We develop a class of rules spanning the range between quadratic dis-criminant analysis and naive Bayes, through a path of sparse graphical models. A group lasso penalty is used to introduce shrinkage and encour-age a similar pattern of sparsity across precision matrices. It gives sparse estimates

Based on the conservation of nucleotides at splic-ing sites and the features of base composition and base correlation around these sites we use the method of increment of diversity combined with quadratic discriminant analysis (IDQD) to study the dependence structure of splicing sites and predict

of misclassification is associated with non-linear (quadratic) discriminant function. Unknown means and the covariance matrices of the feature vector components are estimated from spatially correlated training samples using the maximum likelihood approach and assuming spatial correlations to be known. Explicit formula

Linear and quadratic discriminant analysis are considered in the small sample high-dimensional setting. Alternatives to the usual maximum likelihood (plug-in) estimates for the covariance matrices are proposed. These alternatives are characterized by two parameters, the values of which

. The objective of this paper is twofold. First, we derive an additional kernel tool, namely kernel quadratic discriminant (KQD). We discuss different formulations of KQD based on the regularized kernel Mahalanobis distance in both full and classrelated subspaces. Secondly, we propose suitable extensions

from the bias and covariance of a linear estimator. Class-conditional distributions are modeled locally as Gaussians. Since estimation of Gaussian parameters from few training samples can be illposed, we extend recent work in Bayesian quadratic discriminant analysis to derive a robust local generative