INVESTIGATION OF STATISTICAL LEARNING THEORY FOR MACHINE LEARNING APPLICATIONS

A strong theoretical foundation for analysing the theoretical characteristics of different machine learning algorithms is provided by statistical learning theory (SLT), which is a powerful framework that supports these algorithms. In order to better understand how SLT might be applied to machine learning and how it might affect model performance and generalisation, this study will examine specific applications of SLT in this area. An introduction to the inquiry gives a rundown of SLT's main ideas and mathematical underpinnings. The trade-off between bias and variance, generalisation bounds, and empirical risk reduction are some of the important topics covered. In addition, it looks at the numerous categories of learning algorithms, such as supervised, unsupervised, and reinforcement learning, and how SLT can be used in each of these fields. The examination then focuses on SLT's real-world uses in machine learning. In order to improve model performance and avoid overfitting, it examines how SLT can be used to direct feature engineering, regularisation strategies, and model selection. Additionally, it explores the function of SLT in ensemble learning techniques like bagging and boosting and how they affect the generalisation and durability of models. The inquiry also examines current developments in SLT, including the use of deep learning architectures and the analysis of their behaviour and training dynamics using SLT concepts. It explores the opportunities and difficulties brought on by the high-dimensional and non-linear nature of deep learning models, as well as how SLT might assist in resolving these problems.