Citas bibligráficas
Aguilar, L., Vasquez, Y. (2017). Principal Component Analysis (PCA) para mejorar la performance de aprendizaje de los algoritmos Support Vector Machine (SVM) y Red Neuronal Multicapa (MLNN) [Tesis, Universidad Privada Antenor Orrego]. https://hdl.handle.net/20.500.12759/3398
Aguilar, L., Vasquez, Y. Principal Component Analysis (PCA) para mejorar la performance de aprendizaje de los algoritmos Support Vector Machine (SVM) y Red Neuronal Multicapa (MLNN) [Tesis]. PE: Universidad Privada Antenor Orrego; 2017. https://hdl.handle.net/20.500.12759/3398
@misc{renati/1347958,
title = "Principal Component Analysis (PCA) para mejorar la performance de aprendizaje de los algoritmos Support Vector Machine (SVM) y Red Neuronal Multicapa (MLNN)",
author = "Vasquez Valdivia, Yndra Olenca",
publisher = "Universidad Privada Antenor Orrego",
year = "2017"
}
This thesis explores the problem of data sets with a high number of attributes, and its impact on the learning performance of the algorithms Support Vector Machine (SVM) and Multilayer Neural Network (MLNN). In order to solve this problem we propose the following hypothesis: “The applicat ion of Principal Component Analysis (PCA) over the data set; will improve the learning performance of the algorithms Support Vector Machine (SVM) and Multilayer Neural Network (MLNN)” According wit h our hypothesis; we have the following general object ive: “Improve the learning performance of the algorithms Support Vector Machine (SVM) and Multilayer Neural Network through the application of Principal Component Analysis (PCA) over the data set”. In order to implement the algorithms (SVM, MLNN and PCA), we used the QSAR biodegradation dataset, obtained from the Free Machine Learning Repository (UCI), also all the development of the algorithms was done using Matlab 2014a. Once the algorithms were developed, we begin with the test of our hypothesis, to do so, we create two sets, one applying PCA to the dataset, and the other without applying it, then we measure the learning performance of the algorithms SVM and MLNN against themselves on both datasets (one applying PCA and the other not), at the end, the results show us that both algorithms SVM and MLNN gain a major improvement in their learning performance compared to simple train the algorithms without applying PCA to the dataset.
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