Frontier Integration and Innovation of Mathematical and Statistical Methods in Machine Learning and Image Processing

Abstract

This paper provides a systematic review of how mathematical and statistical methods have played a theoretical guiding role in the fields of machine learning and image processing in recent years. The foundations of modelling and data processing methods are elaborated in detail around various core mathematical frameworks. Innovative architectures of hybrid models are also considered through constructive case studies, revealing the cross-disciplinary collaboration between mathematics and statistics. The outlook for future directions, such as novel hybrid model design, enhanced interpretability, and computational efficiency tuning, will further assist researchers in their work.