An Overview of Non-Deep Networks: A Comprehensive Review

Author(s)	Vishakha Agrawal
Country	United States
Abstract	While deep neural networks have dominated recent machine learning discussions, traditional non-deep networks continue to play a vital role in various applications, offering advantages in interpretability, computational efficiency, and performance on smaller datasets. This comprehensive review examines the landscape of non-deep learning approaches, an- alyzing their theoretical foundations, practical applications, and continuing relevance in modern machine learning. We explore various architectures, from simple perceptrons to sophisticated ensemble methods, highlighting their strengths, limitations, and optimal use cases.
Keywords	Non-deep networks, Support Vector Machine(SVM), Perceptron, Radial Basis Function(RBF), Interpretability
Field	Engineering
Published In	Volume 10, Issue 2, July-December 2019
Published On	2019-12-05
Cite This	An Overview of Non-Deep Networks: A Comprehensive Review - Vishakha Agrawal - IJAIDR Volume 10, Issue 2, July-December 2019. DOI 10.5281/zenodo.14684716
DOI	https://doi.org/10.5281/zenodo.14684716
Short DOI	https://doi.org/g82cb6

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