A Monte Carlo Method for Image Classification Using SVM

Abstract

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.

Automated recognition of asymmetric gait and fatigue gait using ground reaction force data

“…Earlier, Chan et al (Chan et al, 2002) reported 91% and 94% similarity success rates for second-order POLY and linear nuclei in an SVM gender classification task based on gait video sequence data. Our results also show that the SVM model can map the underlying data structures related to asymmetrical and fatigue gait (Atanassov et al, 2021). Machine learning-based classifiers can automatically recognize particular gait patterns according to their measurement methods, which is expected to provide a basis for exploring the potential biomechanical mechanism of running-fatigued.…”