Validate Model Endorsed for Support Vector Machine Alignment with Kernel Function and Depth Concept to Get Superlative Accurateness
Muthukrishnan. R1, Udaya Prakash. N2
1Dr. R. Muthukrishnan, Professor, Department of Statistics, Bharathiar University, Coimbatore. (Tamil Nadu), India.
2N. Udaya Prakash, Research Scholar, Department of Statistics, Bharathiar University, Coimbatore. (Tamil Nadu), India.
Manuscript received on 13 February 2023 | Revised Manuscript received on 20 February 2023 | Manuscript Accepted on 15 March 2023 | Manuscript published on 30 March 2023 | PP: 1-5 | Volume-9 Issue-7, March 2023 | Retrieval Number: 100.1/ijbsac.G0486039723 | DOI: 10.35940/ijbsac.G0486.039723
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© The Authors. Published By: 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: A support vector machine (SVM) is authoritative tool for statistical learning model which is well proved based on the literature reviews which is rooted in finding the operational risk. The Key factor is kernel function and its parameters selection. Once the debate of finalizing the influence factor (i.e) kernel parameters and error penalty factors, we can able to find the new kernel function as a proposed model by bring together the kernel with robust depth procedures. Here the GSOsvm has turn out to be best kernel function with local features to a global representative for any type of dataset. As a final point, experiments are done for dataset with different groups that are formed to show the superior value based on its accuracy on prediction of this kind of model which proves the best validation. Though many research readings suggest the usage of Radial basis (RBF) kernels and polynomial kernels for the conventional techniques, it was found that the results produced by these models have unreliable values, because of the sensitive in the data. The new kernel GSOsvm has the good reliable results both for real and simulated data values precisely when the data contains extreme observations that violated assumptions.
Keywords: Radial Basis Kernel Function, KSVM, Kernel with Weights, Projection Depth
Scope of the Article: Statistics