An Approach through Different Mathematical Models to Enhance the Utility in Different Areas of Machine Learning
Pooja Swaroop Saxena
Abstract
The main purpose of this paper is to elaborate an approach through different Mathematical models to enhance the utility in different areas of Machine learning. Machine learning has vast applications in several sectors such as speech recognition, computer graphics, computer vision ,intelligent control, natural language processing ,decision making, despite the requirement to analyze and interpret data. The present paper explores the application of various mathematical models to enhance the utility and effectiveness of machine learning techniques in different domains. In recent years, due to the ability to analyse and interpret large volumes of data machine learning has gained significant attention. In this research, we investigate different mathematical models that can be integrated into machine learning frameworks to improve their performance in diverse areas. We explore the use of statistical models, such as regression and classification, to develop predictive algorithms that can make accurate predictions based on historical data. Additionally, we delve into the realm of optimization models, such as linear programming and convex optimization, to enhance the efficiency and scalability of machine learning algorithms. Throughout this study, we present practical implementations and empirical evaluations of the proposed mathematical models in various real-world applications, including healthcare, finance, and image recognition. The results demonstrate the significant improvements achieved in terms of accuracy, efficiency, and interpretability, showcasing the value of incorporating diverse mathematical models in machine learning. In the area of machine learning, different strategies have been followed, including unsupervised, supervised algorithms, reinforcement learning and semi-supervised,. In this paper different types of models in different fields were investigated.