Section Article

A Support Vector Machine with a Polynomial Kernel trained on Data from Intrusion Detection Systems
Author(s): Kartik Gupta

Abstract
Knowledge discovery in text (KDT) sometimes referred to as text data mining or text mining is the application of knowledge discovery techniques to unstructured text. This paper presents a proposed support vector classifier that utilises cross validation for the original support vector classifier with a polynomial kernel. If our goal is to enhance the classification accuracy and improve its performance. The recommended approach is demonstrated to be feasible and advantageous by using data sets such as intrusion detection in computer networks. The average accuracy of the recommended support vector machine was more than 28.2% greater compared to the original polynomial-kernel support vector machine. A multitude of concepts and remedies may be implemented across different classifier paradigms since this approach is not dependent on particular data sets.