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The study is conducted for a multiclass soil fertilizer recommendation system for paddy fields. In this study, we propose an improved sigmoid kernel SVM classifier by adjusting the cost and gamma parameters with which a better performance can be achieved. The polynomial kernel seems to be more suitable for performing multiclass SVM classification for the dataset used here. But it is also known that the performance of SVM can be affected by different kernel tricks and regularization parameters like Cost and Gamma. Support Vector Machines (SVM) are advancing rapidly in the field of machine learning due to their enhancing performance in categorization and prediction.
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