A Systematic Literature Survey on Machine Learning
Year: 2024
Type: article
Abstract: This paperwork does a thorough literature review of the various types of machine-learning algorithms, classifying them as supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. This review gives a thorough overview of basic algorithms in each category, covering their theoretical foundations, practical applications, and current advances. This paper tries to provide comprehensive knowledge of the machine learning landscape by highlighting the advantages and disadvantages of various approaches through an analysis of significant research contributions and trends. This literature survey will be a useful resource for researchers, practitioners, and anybody interested in the current state and future directions of machine learning.
Author: Ranjith R, Dr. Srinivasan Arumugam
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6967989679
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