Structure relation between classes for supervised learning using pretopology

Author

Lebourgeois, F. ; Bouayad, M. ; Emptoz, H.

Author_Institution

Lab. de Reconnaissance de Formes et Vision, Inst. Nat. des Sci. Appliquees, Villeurbanne, France

fYear

1999

fDate

20-22 Sep 1999

Firstpage

33

Lastpage

36

Abstract

The article presents a pretopological approach for class proximity evaluation according to cluster boundaries. This is a generic method independent of the classifier used, resulting in a neighborhood structure between objects for each class. This proximity measure of classes also gives an objective quality measure of a given training set and an evaluation of class separability. The theoretical context of the method is presented, followed by an application featuring evaluation for printed character recognition and an example of prototype selection for an improved training set selection

Keywords

character recognition; learning (artificial intelligence); pattern classification; topology; class proximity evaluation; class separability; classifier; cluster boundaries; generic method; neighborhood structure; objective quality measure; pretopological approach; printed character recognition; prototype selection; proximity measure; structure relation; supervised learning; training set; training set selection; Data mining; Density measurement; Electrical capacitance tomography; Pattern recognition; Probability; Prototypes; Reconnaissance; Statistical analysis; Supervised learning; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Document Analysis and Recognition, 1999. ICDAR '99. Proceedings of the Fifth International Conference on

Conference_Location

Bangalore

Print_ISBN

0-7695-0318-7

Type

conf

DOI

10.1109/ICDAR.1999.791718

Filename

791718