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Soft Computing Techniques Based Image Classification using Support Vector Machine Performance

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Soft Computing Techniques Based Image Classification using Support Vector Machine Performance


Tarun Jaiswal | Dr. S. Jaiswal | Dr. Ragini Shukla


https://doi.org/10.31142/ijtsrd23437


Tarun Jaiswal | Dr. S. Jaiswal | Dr. Ragini Shukla "Soft Computing Techniques Based Image Classification using Support Vector Machine Performance" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3, April 2019, pp.1645-1650, URL: https://www.ijtsrd.com/papers/ijtsrd23437.pdf

n 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 ways- time 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.

SVM, PSD, time series; support vector machine; dynamic time warping; kernel method


IJTSRD23437
Volume-3 | Issue-3, April 2019
1645-1650
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

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