Automated Classification using SVM and Back Propogation Learning Technique
Shital Solanki1, Ramesh Prajapati2
1Prof. Shital Solanki*, Assistant Professor, L. D. Engineering College, Ahmedabad, Gujarat Technological University, Gujarat ,India.
2Dr.Ramesh Prajapati, Assistant Professor, Indrashil Institute of Science & Technology, Rajpur- Kadi, Gujarat Technological University, Gujarat India.
Manuscript received on March 15, 2020. | Revised Manuscript received on April 01, 2020. | Manuscript published on April 10, 2020. | PP: 763-766 | Volume-9 Issue-6, April 2020. | Retrieval Number: C8350019320/2020©BEIESP | DOI: 10.35940/ijitee.C8350.049620
Open Access | Ethics and Policies | Cite | Mendeley
© 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: In this paper the comparative study of two supervised machine learning techniques for classification problems has been done. Due to the real-time processing ability of neural network, it is having numerous applications in many fields. SVM is also very popular supervised learning algorithm because of its good generalization power. This paper presents the thorough study of the presented classification algorithm and their comparative study of accuracy and speed which would help other researchers to develop novel algorithms for applications. The comparative study showed that the performance of SVM is better when dealing with multidimensions and continuous features. The selection and settings of the kernel function are essential for SVM optimality.
Keywords: Artificial Neural Network, Generalization, Nonlinearity, Support vector machine, Supervised learning
Scope of the Article: Artificial Intelligence and machine learning