The pattern recognition literature is replete with the use of principal component analysis in the interpretation and analysis of data. However, in the specific case of classification, especially of biomedical patterns, this pre-processing method, which transforms possibly correlated features into a new set of uncorrelated variables, must be used with caution since a principal component, which m...

Design patterns describe good solutions to common and recurring problems. The applications of design patterns may vary in different layouts, which pose challenges for recovering and changing these design pattern instances since essential characteristics of each design pattern are described implicitly. In this paper, we categorize different characteristics of each design pattern as its traits in...

A useful discriminant vector for pattern classification is one that maximizes the minimum separation of discriminant function values for two pattern classes. This optimality criterion can prove valuable in many situations because it emphasizes the class elements that are most difficult to classify. A method for computing this discriminant vector by quadratic programming is derived. The resultin...

A method is proposed for finding decision boundaries, approximated by piecewise linear segments, for the classification of patterns in R 2, using an elitist model of a genetic algorithm. It involves the generation and placement of a set of lines (represented by strings) in the feature space that yields minimum misclassification. The effectiveness of the algorithm is demonstrated, for different ...

Classical and recent results in statistical pattern recognition and learning theory are reviewed in a two-class pattern classification setting. This basic model best illustrates intuition and analysis techniques while still containing the essential features and serving as a prototype for many applications. Topics discussed include nearest neighbor, kernel, and histogram methods, Vapnik–Chervone...

A method is described for finding decision boundaries, approximated by piecewise linear segments, for classifying patterns in ~N,N >~ 2, using Simulated Annealing (SA). It involves generation and placement of a set of hyperplanes (represented by strings) in the feature space that yields minimum misclassification. Theoretical analysis shows that as the size of the training data set approaches in...

The basic element in the solution of pattern-recognition problems is the requirement for the ability to recognize membership in classes. This report considers the automatic establishment of decision criteria for measuring membership in classes that are known only from a finite set of samples. Each sample is represented by a point in a suitably chosen, finite-dimensional vector space in which a ...

Previous sphere-based classification algorithms usually need a number of spheres in order to achieve good classification performance. In this paper, inspired by the support vector machines for classification and the support vector data description method, we present a new method for constructing single spheres that separate data with the maximum separation ratio. In contrast to previous methods...

It is well known that for certain tasks, quantum computing outperforms classical computing. A growing number of contributions try to use this advantage in order to improve or extend classical machine learning algorithms by methods of quantum information theory. This paper gives a brief introduction into quantum machine learning research using the example of pattern classification. We present a ...

In this paper, we propose a new classification method using composite features, each of which consists of a number of primitive features. The covariance of two composite features contains information on statistical dependency among multiple primitive features. A new discriminant analysis (C-LDA) using the covariance of composite features is a generalization of the linear discriminant analysis (...