Advancing Machine Learning: Comparative Analysis of Techniques

EasyChair Preprint 15546

Abstract

This study examines recent advancements in Machine Learning (ML) by comparing various techniques, including both supervised and unsupervised learning methods. It provides a comprehensive review of popular algorithms, evaluating their performance across different domains. By leveraging mathematical models and experimental results, the analysis highlights how these methods address real-world challenges, with a focus on accuracy, efficiency, and scalability. The findings shed light on the strengths and limitations of each approach, offering practical insights for researchers and practitioners seeking to enhance ML model performance across diverse applications.

Keyphrases: Algorithms, analysis, machine learning, model

@booklet{EasyChair:15546,
  author    = {Alex Wang and Maria Kin and Piter Wen and Rusel Deniz and Li Wei and Michael Lornwood},
  title     = {Advancing Machine Learning: Comparative Analysis of Techniques},
  howpublished = {EasyChair Preprint 15546},
  year      = {EasyChair, 2024}}