Title: An Architecture Combining Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for Image Classification,
Abstract: Convolutional neural networks (CNNs) are similar to "ordinary" neural
networks in the sense that they are made up of hidden layers consisting of
neurons with "learnable" parameters. These neurons receive inputs, performs a
dot product, and then follows it with a non-linearity. The whole network
expresses the mapping between raw image pixels and their class scores.
Conventionally, the Softmax function is the classifier used at the last layer
of this network. However, there have been studies (Alalshekmubarak and Smith,
2013; Agarap, 2017; Tang, 2013) conducted to challenge this norm. The cited
studies introduce the usage of linear support vector machine (SVM) in an
artificial neural network architecture. This project is yet another take on the
subject, and is inspired by (Tang, 2013). Empirical data has shown that the
CNN-SVM model was able to achieve a test accuracy of ~99.04% using the MNIST
dataset (LeCun, Cortes, and Burges, 2010). On the other hand, the CNN-Softmax
was able to achieve a test accuracy of ~99.23% using the same dataset. Both
models were also tested on the recently-published Fashion-MNIST dataset (Xiao,
Rasul, and Vollgraf, 2017), which is suppose to be a more difficult image
classification dataset than MNIST (Zalandoresearch, 2017). This proved to be
the case as CNN-SVM reached a test accuracy of ~90.72%, while the CNN-Softmax
reached a test accuracy of ~91.86%. The said results may be improved if data
preprocessing techniques were employed on the datasets, and if the base CNN
model was a relatively more sophisticated than the one used in this study.