Exploring the Applications of DC Programming in Support Vector Machine Learning

DC programming, a method for breaking down complex optimization problems into simpler ones, and Support Vector Machines, a learning algorithm, are both widely used techniques in the field of optimization and machine learning. When combined, these techniques have been proven to be effective in solving various optimization problems, particularly those related to classification and feature selection. This paper highlights the key uses of DC programming in the context of Support Vector Machines (SVMs) for classification, presents the current state-of-the-art results and recent advancements and sheds light on the potential of DC programming as a powerful tool in solving complex optimization problems and its ability to improve the performance of SVMs. This literature review will be of interest to researchers and practitioners working in the fields of optimization and machine learning, particularly those interested in classification problems.