Abstract :
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. 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, URL: https://www.ijtsrd.com/papers/ijtsrd23437.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/23437/soft-computing-techniques-based-image-classification-using-support-vector-machine-performance/tarun-jaiswal
Keyword :
SVM, PSD, timeseries;supportvectormachine;dynamictimewarping;kernelmethod