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Holomorphic functional calculus in A -pseudoconvex algebras

Maciej Łuczak — 2005

Commentationes Mathematicae

We define and study a holomorphic functional calculus for a single element in complex and real complete A-pseudoconvex algebras with unit. As a consequence of the main result we obtain the spectral mapping theorem and existence of the logarithm and the nth root of an algebra element.

A variant of gravitational classification

Tomasz GóreckiMaciej Luczak — 2014

Biometrical Letters

In this article there is proposed a new two-parametrical variant of the gravitational classification method. We use the general idea of objects' behavior in a gravity field. Classification depends on a test object's motion in a gravity field of training points. To solve this motion problem, we use a simulation method. This classifier is compared to the 1NN method, because our method tends towards it for some parameter values. Experimental results on different data sets demonstrate an improvement...

Some methods of constructing kernels in statistical learning

Tomasz GóreckiMaciej Łuczak — 2010

Discussiones Mathematicae Probability and Statistics

This paper is a collection of numerous methods and results concerning a design of kernel functions. It gives a short overview of methods of building kernels in metric spaces, especially R n and S n . However we also present a new theory. Introducing kernels was motivated by searching for non-linear patterns by using linear functions in a feature space created using a non-linear feature map.

Linear discriminant analysis with a generalization of the Moore-Penrose pseudoinverse

Tomasz GóreckiMaciej Łuczak — 2013

International Journal of Applied Mathematics and Computer Science

The Linear Discriminant Analysis (LDA) technique is an important and well-developed area of classification, and to date many linear (and also nonlinear) discrimination methods have been put forward. A complication in applying LDA to real data occurs when the number of features exceeds that of observations. In this case, the covariance estimates do not have full rank, and thus cannot be inverted. There are a number of ways to deal with this problem. In this paper, we propose improving LDA in this...

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