Fontanella, Francesco (2006) An Approach to Pattern Recognition by Evolutionary Computation. [Tesi di dottorato] (Inedito)

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Tipologia del documento: Tesi di dottorato
Lingua: English
Titolo: An Approach to Pattern Recognition by Evolutionary Computation
Autori:
AutoreEmail
Fontanella, Francesco[non definito]
Data: 2006
Tipo di data: Pubblicazione
Numero di pagine: 165
Istituzione: Università degli Studi di Napoli Federico II
Dipartimento: Informatica e sistemistica
Dottorato: Ingegneria informatica ed automatica
Ciclo di dottorato: 18
Coordinatore del Corso di dottorato:
nomeemail
Cordella, Luigi Pietro[non definito]
Tutor:
nomeemail
Cordella, Luigi[non definito]
Data: 2006
Numero di pagine: 165
Parole chiave: Evolutionary Computation, Pattern Recognition, Prototype Generation
Settori scientifico-disciplinari del MIUR: Area 09 - Ingegneria industriale e dell'informazione > ING-INF/05 - Sistemi di elaborazione delle informazioni
Depositato il: 31 Lug 2008
Ultima modifica: 04 Dic 2014 08:10
URI: http://www.fedoa.unina.it/id/eprint/576
DOI: 10.6092/UNINA/FEDOA/576

Abstract

Evolutionary Computation has been inspired by the natural phenomena of evolution. It provides a quite general heuristic, exploiting few basic concepts: reproduction of individuals, variation phenomena that affect the likelihood of survival of individuals, inheritance of parents features by offspring. EC has been widely used in the last years to effectively solve hard, non linear and very complex problems. Among the others, EC–based algorithms have also been used to tackle classification problems. Classification is a process according to which an object is attributed to one of a finite set of classes or, in other words, it is recognized as belonging to a set of equal or similar entities, identified by a label. Most likely, the main aspect of classification concerns the generation of prototypes to be used to recognize unknown patterns. The role of prototypes is that of representing patterns belonging to the different classes defined within a given problem. For most of the problems of practical interest, the generation of such prototypes is a very hard problem, since a prototype must be able to represent patterns belonging to the same class, which may be significantly dissimilar each other. They must also be able to discriminate patterns belonging to classes different from the one that they represent. Moreover, a prototype should contain the minimum amount of information required to satisfy the requirements just mentioned. The research presented in this thesis, has led to the definition of an EC–based framework to be used for prototype generation. The defined framework does not provide for the use of any particular kind of prototypes. In fact, it can generate any kind of prototype once an encoding scheme for the used prototypes has been defined. The generality of the framework can be exploited to develop many applications. The framework has been employed to implement two specific applications for prototype generation. The developed applications have been tested on several data sets and the results compared with those obtained by other approaches previously presented in the literature.

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