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Support vector machine (SVM) is a powerful technique for data classification. Despite of its good theoretic foundations and high classification accuracy, normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is highly dependent on the size of data set. This paper presents a novel SVM classification approach(More)
Support vector machine (SVM) has been successfully applied to solve a large number of classification problems. Despite its good the-oretic foundations and good capability of generalization, it is a big challenging task for the large data sets due to the training complexity, high memory requirements and slow convergence. In this paper, we present a new(More)
— Despite of good theoretic foundations and high classification accuracy of support vector machine (SVM), normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is very high. This paper presents a novel two stages SVM classification approach for large data sets by randomly selecting training data. The first(More)
—Support Vector Machines (SVM) have gained profound interest amidst the researchers. One of the important issues concerning SVM is with its application to large data sets. It is recognized that SVM is computationally very intensive. This paper presents a novel multi SVM classification approach for large data sets using the sketch of classes distribution(More)
  • Mexico City, Mexico September, Jair Cervantes, Xiaoou Li, Wen Yu, Javier Bejarano
  • 2007
— Support Vector Machines (SVM) for binary classification have been developed in a broad field of applications. But normal SVM algorithms are not suitable for classification of large data sets because of high training complexity. This paper introduces a novel two-stage SVM classification approach for large data sets: minimum enclosing ball (MEB) clustering(More)
In this paper it is introduced a recognition method for Mexican banknotes by using artificial vision. It is shown that the Mexican banknotes can be classified by extracting their color and texture features, with the RGB space and the Local Binary Patterns, respectively. We show the classification results performed with the current Mexican banknotes. We(More)