Soft Computing Techniques Based Image Classification using Support Vector Machine Performance
Year: 2019
Type: article
Abstract: In this paper we compare different kernel had been developed for support vector machine based time series classification.Despite the better presentation of Support Vector Machine (SVM) on many concrete classification problems, the algorithm is not directly applicable to multi-dimensional routes having different measurements.Training support vector machines (SVM) with indefinite kernels has just fascinated consideration in the machine learning public.This is moderately due to the fact that many similarity functions that arise in practice are not symmetric positive semidefinite.In this paper, by spreading the Gaussian RBF kernel by Gaussian elastic metric kernel.Gaussian elastic metric kernel is extended version of Gaussian RBF.The extended version divided in two waystime wrap distance and its real penalty.Experimental results on 17 datasets, time series data sets show that, in terms of classification accuracy, SVM with Gaussian elastic metric kernel is much superior to other kernels, and the ultramodern similarity measure methods.In this paper we used the indefinite resemblance function or distance directly without any conversion, and, hence, it always treats both training and test examples consistently.Finally, it achieves the highest accuracy of Gaussian elastic metric kernel among all methods that train SVM with kernels i.e. positive semi-definite (PSD) and Non-PSD, with a statistically significant evidence while also retaining sparsity of the support vector set.
DOI: 10.31142/ijtsrd23437
Institutions: Guru Ghasidas Vishwavidyalaya, Dr. C. V. Raman University
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W2949335684