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A General Framework for Relaxation Processes by

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Název: A General Framework for Relaxation Processes by
Autoři: Petko Faber
Přispěvatelé: The Pennsylvania State University CiteSeerX Archives
Zdroj: https://www.inf.ed.ac.uk/publications/online/0057.pdf.
Rok vydání: 2001
Sbírka: CiteSeerX
Témata: generalization, compatibility function, support function, relaxation operator, convergence
Popis: This paper addresses a major problem in pattern recognition: the unambiguous identification of objects. Comparing the characteristics of objects with the characteristics of possible interpretations, the identification of objects is frequently ambiguous. To reduce ambiguous assignments one can include a) more characteristics or b) contextual information into the identification. We use contextual information. Various approaches are suggested in the literature to describe and generalize discrete or continuous relaxation processes concerning to several objectives and one can find a suitable approach for every problem. The wider problem addressed here is to find the best-suited relaxation process for a given assignment problem or, better still, to construct a task-dependent relaxation process. In this paper we describe an approach to generalizing relaxation processes. For this purpose, we develop a general framework for the theoretical foundations of relaxation processes in pattern recognition. The resulting structure enables 1) a description of all known relaxation processes in general terms and 2) the design of task-dependent relaxation processes. We show that the well-known standard relaxation formulas verify our approach.
Druh dokumentu: text
Popis souboru: application/pdf
Jazyk: English
Relation: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.330.9139
Dostupnost: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.330.9139
Rights: Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Přístupové číslo: edsbas.3E183B42
Databáze: BASE
Popis
Abstrakt:This paper addresses a major problem in pattern recognition: the unambiguous identification of objects. Comparing the characteristics of objects with the characteristics of possible interpretations, the identification of objects is frequently ambiguous. To reduce ambiguous assignments one can include a) more characteristics or b) contextual information into the identification. We use contextual information. Various approaches are suggested in the literature to describe and generalize discrete or continuous relaxation processes concerning to several objectives and one can find a suitable approach for every problem. The wider problem addressed here is to find the best-suited relaxation process for a given assignment problem or, better still, to construct a task-dependent relaxation process. In this paper we describe an approach to generalizing relaxation processes. For this purpose, we develop a general framework for the theoretical foundations of relaxation processes in pattern recognition. The resulting structure enables 1) a description of all known relaxation processes in general terms and 2) the design of task-dependent relaxation processes. We show that the well-known standard relaxation formulas verify our approach.