A Monte Carlo Method for Image Classification Using SVM

Support Vector Machines are a widely used tool in Machine Learning. They have some important advantages with regards to the more popular Deep Neural Networks. For the problem of image classification, multiple SVMs may be used and the issue of finding the best hyperparameters adds additional complexity and increases the overall computational time required. Our goal is to develop and study Monte Carlo algorithms that allow faster discovery of good hyperparameters and training of the SVMs, without impacting negatively the final accuracy of the models. We also employ GPUs and parallel computing in order to achieve good utilisation of the capabilities of the available hardware. In this paper we describe our methods, provide implementation details and show numerical results, achieved on the publicly available Architectural Heritage Elements image Dataset.