Quantification of pattern recognition quality by multivariate normal distribution functions

Screening for a Multivariate Mixture Normal Distribution

Uncertainty Quantification by Alternative Decompositions of Multivariate Functions

This article advocates factorized and hybrid dimensional decompositions (FDD/HDD), as alternatives to analysis-of-variance dimensional decomposition (ADD), for second-moment statistical analysis of multivariate functions. New formulas revealing the relationships between component functions of FDD and ADD are proposed. While ADD or FDD is relevant when a function is strongly additive or strongly...

On the Quality of Decision Functions in Pattern Recognition

The problem of decision functions quality in pattern recognition is considered. An overview of the approaches to the solution of this problem is given. Within the Bayesian framework, we suggest an approach based on the Bayesian interval estimates of quality on a finite set of events.

On the Concavity of Multivariate Probability Distribution Functions on the Concavity of Multivariate Probability Distribution Functions

We prove that the multivariate standard normal probability distribution function is concave for large argument values. The method of proof allows for the derivation of similar statements for other types of multivariate probability distribution functions too. The result has important application, e.g., in probabilistic constrained stochastic programming problems.

Pattern Recognition Using Morphological Class Distribution Functions and Classification Trees

The paper presents an effective and robust method of classifying binary patterns. It starts with classification of foreground pixels of binary image into several spatial classes, which is performed using morphological image processing. By performing this classification with structuring elements of increasing sizes, the spatial class distribution functions are produced. These functions are norma...