Enhancing Machine Learning: a Comparative Review of Techniques

EasyChair Preprint 15685

Abstract

This study explores recent advancements in Machine Learning (ML) through a comparative analysis
of various techniques, encompassing both supervised and unsupervised learning methods. It
presents a thorough review of widely used algorithms, assessing their performance in different
domains. By employing mathematical models and experimental results, the analysis underscores
how these techniques tackle real-world challenges, with an emphasis on accuracy, efficiency, and
scalability. The findings provide insights into the strengths and limitations of each approach, offering
valuable guidance for researchers and practitioners aiming to optimize ML model performance
across a variety of applications.

Keyphrases: Algorithms, analysis, machine learning, model

@booklet{EasyChair:15685,
  author    = {Ahmad Hossein},
  title     = {Enhancing Machine Learning: a Comparative Review of Techniques},
  howpublished = {EasyChair Preprint 15685},
  year      = {EasyChair, 2025}}