A Survey on Various Approaches for Support Vector Machine Based Engineering Applications
Khushboo Nagar1, M.P.S. Chawla2
1Khushboo Nagar, Assistant Professor, Department of Electrical Engineering, Shri G. S. Institute of Technology & Science, Indore (M.P), India.
2M.P.S. Chawla, Associate Professor, Department of Electrical Engineering, Shri G. S. Institute of Technology & Science, Indore (M.P), India.
Manuscript received on 12 September 2023 | Revised Manuscript received on 21 September 2023 | Manuscript Accepted on 15 October 2023 | Manuscript published on 30 October 2023 | PP: 6-11 | Volume-11 Issue-11, October 2023 | Retrieval Number: 100.1/ijese.K255510111123 | DOI: 10.35940/ijese.K2555.1011112
Open Access | Editorial and Publishing Policies | Cite | Zenodo | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Support vector machines describe a system that uses a feature space with a hypothesis space of linear functions that is trained using various learning algorithms from optimization theory. This paper provides a brief introduction to SVM and a survey of different methods applied to obtain results using classifiers. The aim is to classify and get results for other classes of points using various SVM classifiers and to justify the results using methods such as Gaussian Kernel and Custom Kernel, as well as Cross-Validation to assess the functioning of SVM classifiers through Posterior Probability Regions for SVM classification models with different types of data.
Keywords: Support Vector Machine, Optimization Algorithms, Classifiers, Hyperplane, SVM Application, Pattern Recognition, Face Detection
Scope of the Article: Support Vector Machine