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Kernel Ho-Kashyap classifier with generalization control

Jacek Łęski (2004)

International Journal of Applied Mathematics and Computer Science

This paper introduces a new classifier design method based on a kernel extension of the classical Ho-Kashyap procedure. The proposed method uses an approximation of the absolute error rather than the squared error to design a classifier, which leads to robustness against outliers and a better approximation of the misclassification error. Additionally, easy control of the generalization ability is obtained using the structural risk minimization induction principle from statistical learning theory....

KIS: An automated attribute induction method for classification of DNA sequences

Rafał Biedrzycki, Jarosław Arabas (2012)

International Journal of Applied Mathematics and Computer Science

This paper presents an application of methods from the machine learning domain to solving the task of DNA sequence recognition. We present an algorithm that learns to recognize groups of DNA sequences sharing common features such as sequence functionality. We demonstrate application of the algorithm to find splice sites, i.e., to properly detect donor and acceptor sequences. We compare the results with those of reference methods that have been designed and tuned to detect splice sites. We also show...

Knowledge revision in Markov networks.

Jörg Gebhardt, Christian Borgelt, Rudolf Kruse, Heinz Detmer (2004)

Mathware and Soft Computing

A lot of research in graphical models has been devoted to developing correct and efficient evidence propagation methods, like join tree propagation or bucket elimination. With these methods it is possible to condition the represented probability distribution on given evidence, a reasoning process that is sometimes also called focusing. In practice, however, there is the additional need to revise the represented probability distribution in order to reflect some knowledge changes by satisfying new...

Knowledge sharing in organizational structures

Ivo Vondrák, Václav Snášel, Jan Kozušzník (2004)

Kybernetika

The organizational structure is usually defined using the best experience and there is a minimum of formal approach involved. This paper shows the possibilities of the theory of concept analysis that can help to understand organizational structure based on solid mathematical foundations. This theory is extended by the concept of knowledge sharing and diversity that enables to evaluate the organizational structure. The alternative approach based on the hierarchical methods of cluster analysis is...

Knowledge vagueness and logic

Urszula Wybraniec-Skardowska (2001)

International Journal of Applied Mathematics and Computer Science

The aim of the paper is to outline an idea of solving the problem of the vagueness of concepts. The starting point is a definition of the concept of vague knowledge. One of the primary goals is a formal justification of the classical viewpoint on the controversy about the truth and object reference of expressions including vague terms. It is proved that grasping the vagueness in the language aspect is possible through the extension of classical logic to the logic of sentences which may contain vague...

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