Performance Evaluation Of Support Vector Machines (Svms)And Convolutional Neural Network (Cnn) On Binary Classification Problem
Journal Title: International Journal of engineering Research and Applications - Year 2018, Vol 8, Issue 9
Abstract
Support vector machines (SVMs) have been around for decades, they have been used for a number of classification tasks. They actually have a very strong theory behind them, which make it relatively easy to choose the best hyper-parameters. The kernel trick makes it easier for SVMs to implicitly classify in higher dimensional space, making it possible to work with nonlinearly separable datasets. On the other hand, Convolutional Neural Networks (CNNs) have gained important attention in recent years for their high performance in image classification problems with high number of categories. The automatic feature extraction of convolutional layer and the dimensionality reduction of the pooling layer make CNN gain high predictive power on testing data. In this work both models are briefly discussed and implemented on a binary classification problem from the EMIST character dataset. The CNN outperformed SVM achieving a misclassification error rate on test data of 1.7 % against 2.32 % for SVM.
Authors and Affiliations
Abdelaziz Botalb
Keywords
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- DOI 10.9790/9622-0809044448.
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