The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Machine learning algorithms review
Open accessAbstract
Machine learning is a field of study where the computer can learn for itself without a human explicitly hardcoding the knowledge for it. These algorithms make up the backbone of machine learning. This paper aims to study the field of machine learning and its algorithms. It will examine different types of machine learning models and introduce their most popular algorithms. The methodology of this paper is a literature review, which examines the most commonly used machine learning algorithms in the current field. Such algorithms include Nave Bayes, Decision Tree, KNN, and K-Mean Cluster. Nowadays, machine learning is everywhere and almost everyone using a technology product is enjoying its convenience. Applications like spam mail classification, image recognition, personalized product recommendations, and natural language processing all use machine learning algorithms. The conclusion is that there is no single algorithm that can solve all the problems. The choice of the use of algorithms and models must depend on the specific problem.
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Citations
Cites
Decision tree methods: applications for classification and prediction.
Song, Y. Y., & Lü, Y. (2015) · PubMed · doi:10.11919/j.issn.1002-0829.215044 · Cited by 1,132
Algorithms for Reinforcement Learning
Szepesvári, C. (2010) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.1007/978-3-031-01551-9 · Cited by 1,049
Neural Networks and Deep Learning
(2016) · Computer Age Statistical Inference · doi:10.1017/cbo9781316576533.019 · Cited by 509
Overview of use of decision tree algorithms in machine learning
Navada, A., Ansari, A. N., Patil, S., & Sonkamble, B. A. (2011) · 2011 IEEE Control and System Graduate Research Colloquium · doi:10.1109/icsgrc.2011.5991826 · Cited by 383
Neural Networks and Deep Learning
Nielsen, M. A. (2015) · Cited by 272
Cited by
Classification Based on Decision Tree Algorithm for Machine Learning
Charbuty, B., & Abdulazeez, A. (2021) · Journal of Applied Science and Technology Trends · doi:10.38094/jastt20165 · Cited by 1,871
A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science
Alloghani, M., Al-Jumeily, D., Mustafina, J., Hussain, A., & Aljaaf, A. J. (2020) · Unsupervised and Semi-Supervised Learning · doi:10.1007/978-3-030-22475-2_1 · Cited by 702
Machine learning-based approach: global trends, research directions, and regulatory standpoints
Pugliese, R., Regondi, S., & Marini, R. (2021) · Data Science and Management · doi:10.1016/j.dsm.2021.12.002 · Cited by 373
Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats
Dobbelaere, M. R., Plehiers, P. P., Van de Vijver, R., Stevens, C. V., & Van Geem, K. M. (2021) · Engineering · doi:10.1016/j.eng.2021.03.019 · Cited by 323
Khalil, M., McGough, A. S., Pourmirza, Z., Pazhoohesh, M., & Walker, S. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105287 · Cited by 299
Metadata
- Authors
- Ling, Q.
- Venue
- Applied and Computational Engineering
- Publisher
- EWA Publishing
- Published
- 2023-05-31
- Volume
- 4
- Issue
- 1
- Pages
- 91-98
- ISSN
- 2755-2721 (ISSN-L)
- 2755-273X
- OpenAlex
- W4383560187
- Cited by
- 465
- References
- 5
- FWCI
- 50.99
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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