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Listar Capítulos de Libro por autor "García Lamont, Farid"

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  • A hybrid algorithm to improve the accuracy of support vector machines on skewed data-sets 
    Cervantes Canales, Jair; García Lamont, Farid; LOPEZ CHAU, ASDRUBAL; Huang, De-Shuang (Springer, 2014)
    Over the past few years, has been shown that generalization power of Support Vector Machines (SVM) falls dramatically on imbalanced data-sets. In this paper, we propose a new method to improve accuracy of SVM on imbalanced ...
  • Classification of mexican paper currency denomination by extracting their discriminative colors 
    Cervantes Canales, Jair; García Lamont, Farid; LOPEZ CHAU, ASDRUBAL; Rodríguez Mazahua, Lisbeth (Springer, 2013)
    In this paper we describe a machine vision approach to recognize the denomination classes of the Mexican paper currency by extracting their color features. A banknote’s color is characterized by summing all the color vectors ...
  • Classification on imbalanced data sets, taking advantage of errors to improve performance 
    Cervantes Canales, Jair; García Lamont, Farid; LOPEZ CHAU, ASDRUBAL (Springer, 2015)
    Classification methods usually exhibit a poor performance when they are applied on imbalanced data sets. In order to overcome this problem, some algorithms have been proposed in the last decade. Most of them generate ...
  • Color characterization comparison for machine vision-based fruit recognition 
    Cervantes Canales, Jair; García Lamont, Farid; LOPEZ CHAU, ASDRUBAL; RUIZ CASTILLA, JOSE SERGIO (Springer, 2015)
    In this paper we present a comparison between three color characterizations methods applied for fruit recognition, two of them are selected from two related works and the third is the authors’ proposal; in the three works, ...
  • PSO-based method for svm classification on skewed data-sets 
    Cervantes Canales, Jair; García Lamont, Farid; Rodríguez Mazahua, Lisbeth; LOPEZ CHAU, ASDRUBAL; RUIZ CASTILLA, JOSE SERGIO; Trueba Espinosa, Adrián (Neurocomputing, 2016-11-01)
    Support Vector Machines (SVM) have shown excellent generalization power in classification problems. However, on skewed data-sets, SVM learns a biased model that affects the classifier performance, which is severely damaged ...

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