The paper presents a comparative analysis of various types of normalization techniques. The accuracy of data classification which was carried out after data normalizing was used as the main criterion for evaluating the quality of the appropriate normalizing method. Four various types of datasets downloaded from the UCI Machine Learning Repository were used as the experimental data during the simulation process. Various normalization techniques available from package clus-terSim of R software were applied to the experimental data. The quality of the data normalizing procedure was evaluated based on the use of data classification by the calculation of the accuracy of the objects distribution into classes. The neural network multilayer perceptron was used as the classifier at this step. The simulation results have shown that the data normalizing stage significantly influences the classification accuracy and selection of the normalization method depends on the type of data and, consequently, the selection of the normalizing technique should be carried out in each of the cases separately.
El artículo describe la eficacia del enfoque basado en corpus en la enseñanza del inglés como lengua extranjera. Los autores se concentran en la noción de "corpus de texto" y la definen como datos unificados, estructurados y etiquetados con parte del discurso en forma electrónica que se utilizan para ciertas investigaciones filológicas y, más ampliamente, humanitarias. Los enfoques principales de la Lingüística del Corpus son humanistas, inductivos, orientados conscientemente, deductivos. El modelo alternativo basado en la capacidad de encapsular, es decir, "aprender usando una base de datos" le da a cualquier estudiante acceso a numerosos hechos del uso del lenguaje y le deja al estudiante todo el trabajo de analizar estos hechos y encontrar respuestas.
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