Published October 30, 2023 | Version CC BY-NC-ND 4.0
Journal article Open

A Survey on Various Approaches for Support Vector Machine Based Engineering Applications

Creators

  • 1. Assistant Professor, Department of Electrical Engineering, Shri G. S. Institute of Technology & Science, Indore (M.P), India.

Contributors

Contact person:

Researcher:

  • 1. Assistant Professor, Department of Electrical Engineering, Shri G. S. Institute of Technology & Science, Indore (M.P), India.
  • 2. Associate Professor, Department of Electrical Engineering, Shri G. S. Institute of Technology & Science, Indore (M.P), India.

Description

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 presents a brief introduction to SVM, and a survey with different methods applied for obtaining results using classifiers. The aim is to classify and obtain results for different classes of points with different SVM classifiers and to justify the results using various methods like Gaussian Kernel, Custom Kernel, Cross Validate functioning of SVM classifiers through Posterior Probability Regions for SVM classification models with various types of data.

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Additional details

Identifiers

DOI
10.35940/ijese.K2555.10111123
ISSN
2319-6378

Dates

Accepted
2023-10-15
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

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