Special Issue on The Mexican Conference on Pattern Recognition; Guest Editors: José Fco. Martínez-Trinidad (National Institute of Astrophysics, Optics and Electronics, Mexico), Jesús Ariel Carrasco-Ochoa (National Institute of Astrophysics, Optics and Electronics, Mexico), Víctor Ayala-Ramírez (University of Guanajuato, Mexico) and José Arturo Olvera-López (Autonomous University of Puebla (BUAP), Mexico)No Access

Bayesian Learning on Discrete Systems of Two Classes

J. H. Pacheco-Sánchez

Division of Studies of Postgrade and Research, Instituto Tecnológico de Toluca, Av. Tecnológico s/n, Col. Agrícola Bellavista, Metepec, Estado de México 52140, México

E-mail Address: jpachecos@toluca.tecnm.mx

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R. D. Vera-Torres

Division of Studies of Postgrade and Research, Instituto Tecnológico de Toluca, Av. Tecnológico s/n, Col. Agrícola Bellavista, Metepec, Estado de México 52140, México

E-mail Address: robertoveratorres@yahoo.com.mx

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R. Alejo

Division of Studies of Postgrade and Research, Instituto Tecnológico de Toluca, Av. Tecnológico s/n, Col. Agrícola Bellavista, Metepec, Estado de México 52140, México

E-mail Address: ralejoll@hotmail.com

Corresponding author.

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https://doi.org/10.1142/S0218001418600133Cited by:1 (Source: Crossref)

Abstract

Bayesian learning is applied on two class systems. Partitioning a big sample made up of many elements of two classes of indistinguishable objects, we indistinctly pursue from 2 to 5 training sets called hypotheses in the probability field, with a plausible rate of object from each hypothesis. Objects are taken one by one from the sample. The basic aim faced is to predict one type of objects in the following occasion in which an agent takes one of them from the original sample to test it. We obtain the graph of a posteriori probability for each hypothesis of one of the objects. A prediction that the following object is specifically one of them is acquired in one probability curve by means of training previously accomplished. This methodology is applied on manufacture of glass bottles of two classes: good or crash. The main interest is to predict which machine produced one detected crash bottle because bottles turn to be indistinguishable when they are reviewed. This is solved by fixing a priori probabilities and taking into account all possible probability distribution combinations in the classes.

Keywords:

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